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forward. not just three years. >> thank you so much. a key statistic about the amount of time black men spend with their children as fathers, we will put that on our website. i think you might be surprised by these statistics. that's all for this evening. the rachel maddow show starts right now. >> thank you for joining us this hour. in the indictment, they show his real name. tony mack. which is an awesome new jersey politics name. but tony mack apparently was not a cool enough name. according to the indictment, at least, his aliases included napoleon, the little guy, and honey fitz. honey fitz was duly elected in 2010. in the year 2012 the
raided his house. the following day the
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offices at trenton city hall. and in the fall of that year, cement 2012, honey fitz, aka napoleon, aka the little guy waurg arrested with eight other people including the mayor's own brother and the charge was corruption. the feds posed as developers and they offered the mayor a bribe in exchange for him ok'ing the development of a downtown parking garage in trenton. there was no actual planned garage and they were not real developers twaumpl a sting. the mayor said yes to the bribe. in february this month, he was convicted of all charges. he is due to be sentence in the may. and co-get decades in federal prison based on these charges and convictions. on the day he was convicted on february 7th, the associated press provided some helpful contacts for understanding how big of a deal it is for the mayor to be going to jail on federal corruption charges. the short answer is, it is not that big a deal at all. not for new jersey.
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since 2000, the mayors of newark, patterson, hoboken pass aching, asbury park, orange and hamilton, those mayors have all been convicted or pled guilty of corruption. now you can add trenton to the list. you know kory booker as a united states senator. but before becoming a united states senator, he was not just the mayor of newark, new jersey. he was the first mayor of newark, new jersey, since 1962 to not be convicted of corruption charges and to go prison. yeah. new jersey is just astonishing. new jersey is a sewer of public corruption. but since the latest mayor was convicted in new jersey of corruption charges, since hoits fitz tony mack got convicted three fridays ago in trenton,
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what has been astonishing to watch is that he refuses to leave office will until yesterday trenton had as its mayor a man until three weeks earlier had been could not vifktd federal corruption charges and refused to stop being mayor even though he was convicted. trent object had somebody to replace him. they assumed once he was found guilty, he would resign but he refused. it was not until a superior court judge yesterday ruled he must be stripped of his office that he finally became not the mayor. >> you can't have a convict as your mayor. you cannot have someone who has just been found guilty of federal corruption charges and awaiting their time in federal prison serving as your elected leader. this cannot happen. this does not happen. except it does happen in new jersey. what just happened in trenton is not even unprecedented in the state of new jersey. one of the times the camden university mayor went to jail, he too had to forcibly thrown out of office by a judge because he refused to leave.
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refused to stop being mayor. in new jersey, this kind of thing isn't even unprecedented. it is just how it goes. personal point of privilege myself partner susan is from new jersey. i was trying to explain to her about the trenton mayor, trying on explain to her how amazing it was to me that the convicted mayor of trenton was still the mayor of trenton even after he had been convicted. and susan looked at me. she look me right in the eye, dead eyed and said john gorka. in our family is shorthand for the john gorka song, i'm from new jersey which explains this whole thing. ♪ i'm from new jersey ♪ i don't expect too much ♪ if the world ended today ♪ i would adjust
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♪ i would adjust ♪ i would adjust >> i would adjust if the world ended. god bless you, i'm from new jersey. i don't expect too much. new jersey is the place where not only does the mayor of trenlton get convicted. he refuses to leave office. before that the mayor of camden after he was convicted of corruption. when did he that, he was the third mayor of camden in 20 years to be indicted on federal corruption charges. new jersey is the place where the new mayor, the new mayor, the new young freshed placer of hoboken was in jail three weeks best agreed to take a bribe in an
sting. since the previous mayor was in jail at that time as well. new jersey is just a toxic meyer of public corruption.
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and it has been for years. and the people of new jersey, and particularly the cities of new jersey, suffer for the sins of its public officials. parts of camden, new jersey, are worse off than any other single place in the united states. i would put parts of camden against anywhere else we've got in this country in empls the trouble. that's not because new jersey is a poor state. new jersey is a very well off state. the reason that parts of camden look like this is because of governance in new jersey is rotten. it is a place where for generations, the business of new jersey government has not been governance. it has not been the allocation of public resources to serve the greater good. the business of governance in new jersey epically and for way too long has been the business of people in government helping themselves. helping. they to public resources. people who are in government using the power that government gives them to steal what ought to be public resources to use for their own private ends. the great political candle in new jersey that is afflicting
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the governor's hopes is not entirely about that age old new jersey problem. the bridge scandal, nobody appears to have walked away with envelopes stuffed full of cash from those closed access lane on to the george washington brigs ever bridge. the bridge scandal for the chris christie administration is about using a public resource. about using the world's busiest bridge as a weapon to apparently inflict some sort of petty political vendetta which still has not been splanld. at least at this point we haven't seen the profit motive. it is the investigation into that scandal, the thousands of pages of documents that have been handed over and the intense investigative scrutiny from federal prosecutor and especially from the media that has uncovered a newly sordid picture of you how it works. it is not just in camden, not just the bald old days. it is in the richest agencies of
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the state. and yes, it is different than having numbs write about your blatant issues. the problem of polluted governance in new jersey is the same. we got newly unredacted documents today from the new jersey legislature. these same documents that we had earlier. we just have fewer things blacked out with a sharpie marker. one of the lines that used to be blacked out but is not anymore is this one. it is in the middle of a conversation between bill barrone, two who have lost their jobs already. this is after the bridge lane closures, it is in october. they appear to be discussing a nurp article that had covered the closure of those lanes.
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a newspaper article that ran in early okay. we think it was probably an article from the "wall street journal." that's what it seems like from the context. and he says, maria cammella did not think much of the story meaning nobody is paying attention to the story we're talking about. he responded, bridget, same. meaning the staffer in governor christie's office. she also agrees there's nothing much to worry about with the press attention on that bridge. then we get to the line that was previously redacted. david wallstein said, oh, what does general want? the person who everybody calls general in the christie administration is this man. former attorney general david samson who chris christie appointed to be chairman of the port authority after mr. samson was instrumental and he is getting elected to be governor.
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after he headed up chris christie's job to be office. general samson is also a named partner in a powerful new jersey law firm call wolfe. he has done really, really well for himself and gave david samson that job at that very rich agency. this is reporting from wnyc, the radio station. this plots the lobbying income of david samson's law firm over the years. you can see it here for 2007, 2008, 2009. governor christie ran to become governor and was elected in 2009. he resumed office in 2010. and yeah, look, wolff does a lot better once chris christie takes over the state government. and then he appoints samson. and then look, wow, oh, wow, hey. samson is doing really, really well once he gets that gig. these are the numbers for 2012
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as well. david samson has been chairman of the port authority, the port authority has repeatedly taken pakss have financially benefited mr. samson's clients at his private law firm. so for example, his law firm had a client that wanted to make more money on its commuter parking lot that it had in new jersey. a lot of people commute from new jersey into new york. they park their cars and then take some sort of transit into the city. one of those computer lots was owned by the port authority. the port authority toned lot and this other agency operated it. the port authority charged rent in that parking lot. they charged about $900,000 a year rent. so yeah, the entity could make a lot of money off that lot. you charge people $10 a day to park there. they had to pay reynold too. they had to pay rep to the port authority and that reduced their profits. that entity shird david samson's law firm to see how they could
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increase that. so they said the rent should no longer be $900,000 a year. it should be $1 a year. port authority owns that parking lot. that mean the rent they were getting was going to a public agency which means theoretically that money was supposed to go to the good of the public. instead that money will be going to david samson's clients. and they in turn will pay his firm to thank them for having come one this great arrangement. so it is a win/win. from their perspective. david samson's law firm gets paid handsomely for having saved their client almost $1 million a year on the reynold. the only people that lose is everybody else. when the record first reported that story, david samson was on the record of having cast a vote in favor of that deal on the parking lot.
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that deal that so directly benefited his own law firm's client. after that story was published, david samson had the port authority go back in time and recuse him from that vote after the fact. david samson did not even bother to try to retroactively do that. it concerns the town of harrison, new jersey. so mr. samson's law firm has a china that wants to build luxury housing in harrison, new jersey, and it would really help to sell luxury apartments in harrison, new jersey if there was a nice transit stop nearby where you could easily and safely get on the train to new york city. that would really help the
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property values. it totally depends on getting a new train station in harrison. david samson as cheryl of the port authority voted for a new train station in harrison. so i guess that's what public resources should be used to do. whether or not you want harrison, new jersey to have a new train station by all accounts they need a new train station. how can you take a vote on that when your law firm has a client with a financial stake in the outcome which means you have a financial stake in the outcome? we're from new jersey. we don't expect much. those are just a couple of the deals that david samson has been involved with that have turned up since people started looking hard into this. there are the hoboken allegations which are being used. david samson had a china who was working on a poe lengsly big lucrative deal. the mayor of that town said she came under pressure by members of the christie administration
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to greenlight that development for the clients. she said they threatened her. if they did not okay it, they wouldn't get any relief funds from hurricane sandy. those are public funds for the public need. the mayor's office, the christie administration flatly and staunchly denied those funds would elengs being diverted for a private purpose. to try to make david samson's clients make money. and five people have left or lost their jobs in the bridge lane scandal in new jersey krus far. the governor's two manager, two people who worked at the port authority. do you know who still works there? david samson. he is still chairman, still casting votes. as of last night someone who has the unquestioning absolute and complete support of new jersey governor chris christie, no questions asked.
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>> your top port authority appointee, david samson was the target of some criticism director foye. he was asked 20 new york daily news if samson had the moral authority to leave the agency, and he flat out said no. then wouldn't elaborate any further. do you still stand by him? >> strongly. >> governor chris christie speaking last night. not for the first time. he figured out how to convert his public position which was given to him by chris christie into a very lucrative time. john gorka. while chris christie was making that appearance, we got word from the legislative committee investigating the bridge scandal that they were releasing unredacted or less redacted
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versions of the david wildstein document. in a bunch of cases, we just get one line. one new line. so a subtle change. the redacted version on the top and opportunity redacted version on the bottom. we get that new line, what did the general want? apparently referencing the form he attorney general david samson. here again, here's another one between bill barrone and david wildstein. this is right after bill gave his false testimony to the legislate you are with that testimony about a traffic study. right after that we get this new line today that we didn't used to have. kevin o'toole did put out a statement right after the testimony that backed whaum mr. barone said after that. senator o'toole maybe ironically or maybe just weirdly happens to also be the state senator who the republicans assigned to be
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on the legislative committee investigating bridge gate even though he himself is tangentially involve in the scandal. we had heard there was a reference but now we can see it. even though he is on the committee, the co-chairs of the legislative committee investigating this issue say that senator o'toole may actually face questioning on this issue. which would probably mean that he would have to get up off the dais and sit in the witness chair and then come back to the dais later. or maybe i just talked like this. the one thing we got from the redaction is this. this is what we had before. this is the old thing tex bottom of the page, this is where we previously had the messages. they're talking about people who are being delayed on the bridge
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because they shut down those lanes. and bridget kelly saying is it wrong that i'm smiling? that's pretty much all we had on the page before everything else was blocked out. look at the new page. same page but he have different. what was redacted on the whole rest page is now evident. and bizarrely, yes, that is what you think it is. what was redacted on that page includes a picture of john boehner, yes. so in this exchange, this part that they've unredacted, he this banlter a little about this rabbi in this photo with john boehner and what they think of this rabbi. david whilestein says of the rabbi, he has officially pissed me off. and bridget kelly responds we can't cause traffic problems in front of his house, can we? >> flights to tel aviv all mysteriously delayed. they're obviously joking around about this. this is six days after the traffic problems and it is clear
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in this exchange that they're referencing the idea about what they would do on the bridge. they're referring to have will ability to cause traffic problems for people who, i them by using public resources to cause those private pains. and also there are things like they're joking. there is no sign that they did have problems in front of the house nor were flights to tel aviv. it seems clear they're joking. the rabbi has no idea what did he to get david wildstein so mad at him that he was cursing about it. he has no idea why he has appeared in these text messages. i personally would love to hear what john boehner thinks about him turning up in these text messages but so far no one has asked him. he clearly in h nothing to do with it. this is mostly just we're and maybe it goes to the color and flavor that it is. it seal like it is just new jersey politics.
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the big effect here of these redakss is that it may an lesson to all of us to guess what might be under the black magic marker. in some ways that, who would guess it was a random picture of a rabbi with john boehner. you can't know until you see it. but there is so much we don't know here. alpha-hydroxy and exfoliating beads work to clean and tighten pores so they can look half their size. pores...shrink 'em down to size! [ female announcer ] pore refining cleanser. neutrogena®. to help me become an olympian, she was pretty much okay
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i sat there for two hours yesterday with mr. samson, general samson. again, i'm confident that he had no knowledge of this based upon our conversations. and his review of his information. >> your top port authority appointee david samson was the target of criticism by the p.a. exectively corrector patrick foye. he was asked by the new york daily news if samson had the moral authority to leave the agency and he flat out said no but then would not elaborate any further. do you still stand by him as your appointee. >> strongly, firmly. >> chris christie saying in no uncertain terms that he has no problems no, concerns about the form he attorney general of new jersey, who he calls general samson. he appointed him to be chairman of the port authority and who continue in that role today. david samson surfaces again today in these newly unredacted
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documents that have been released by the legislature. he has continued to be the focus some of very critical reporting on his apparent conflicts of interest as chairman of that multi-billion dollar agency. chris christie has no issues with his performance. joining us, ted mann, it is nice to see you. thanks for being here. so chairman samson at the port authority standing his ground. governor christie has repeatedly expressed his steadfast support for him. is that surprising? >> i don't think it is surprising for people who have dealt with david samson who is competent and throughout this has insisted he knew nothing about what the people beneath him were doing and certainly in his public appearance of the most recent port authority board meeting where he said he wasn't going to be able to talk about this and he apologized on behalf of the port authority for inconvenience.
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he really made clear he wasn't planning to go anywhere. >> is it within his, is it his decision and chris christie's decision alone as to whether he goes? >> a really good question. certainly the expectation within the port authority and i think in the political capitals of both states. if chris christie wants him gone, he's gone. certainly there's been no sign from either christie or samson that they think he has to go. there is a tremendous. a pressure on the new york side. obviously pat foye's public comments are part of that. again only foye's has had anything publicly about samson being unfit to lead. >> let me ask you about another angle about you've been writing about. on the day that bill barone delivered that, he talked about feedback and he responded, p.a.d.d., said all was fine. do we know what all was fine? >> we don't know what that means there. in his testimony he made reference to paul who is the head of the police union. he said he was the first person
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who had ever mention ad traffic lane change to david wildstein there. this according to the union came as a surprise that he was going to be mentioned in this context. we don't know there if wildstein is telling barone the actual head of the police department approved of the testimony. we don't know why he would have been weighing in. we don't know if it is a reference to paul. we know on the first time through they blacked that out and that is for the legislate you are and probably the federal prosecutors to try to figure out what the police involvement was. >> and now he has stepped down
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from that role with the police department union. >> there are position is that he was handed someday to day operations to a deputy. he is not going to elaborate but he hasn't stepped down. that he is continuing in his position and we're not sure what that means.
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if you have an instinct to try to troy what you have done while you are in power while you are running away from the mobs and the protesters, try to make sure friendly dictator, try to make sure as you are flinging that evidence off the nearest bridge and into the nearest body of water to destroy it, try to make sure that said evidence does not float. yeah, problems. this is a reservoir near palace that was abandoned in haste by
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the president of ukraine on that opposition movement of his country toppled his government last week after months of protests. floating in the reservoir were thousands of documents. some of them burned. some of them partially burned. some of them still tucked safely inside plastic folders. when the president fled with his entourage and the protesters realize that had the presidential house for the first time could literally be opened to the public, they just tome doors so everybody could walk in and see how he lived. some of the people going through rather ornate groinds noticed there are all these documents floating in the water. they skimmed some of them out of there. the ones they could reach. and over the weekend they had divers go in looking to see if there was more at the bottom of the lake. stuff that had sunk. they pulled up hundreds of files filled with thousands of documents. invoices, contracts, all sorts of stuff. once the documents were fished
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out they were separated and laid out to dry. the really lucky documents got laid out to dry in the sauna in the very nice guest house. now citizens and journalists are going through the documents one by one to figure out what they've got and what the documents can tell them about the government they just toppled. a lot of them have been about the finances of how the ousted president lived, what he spent, apparently even some public money on at a time when ukraine's economy was in shambles. this recovered document shows that he commissioned a roaring boar decoration for his outdoor shooting range. apparent lay $93,000 boar. $17,000 for seven table cloths. another one shows a receipt for $12 million in cash from a presidential representative to some other person. there is also some evidence of the president monitoring specific journalists and a very
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personal & very personal levels. now journalists have been sifting through these documents trying to see what it was the president tried to burn and then drown as he was fleeing kiev. they've now launch ad website. basically a play on wikileaks but named for the deposed president. on the website they're uploading and cataloguing trying to an over tate these so the public can scroll through and see what their president did. they're post noug as quick as they can the ideas is to post first and then launch investigations later. almost 1,600 documents have been posted online and more are expected soon. this is far from over. the work continues. may not while there is a question of whether or not the president who fled the capital and left all this stuff in the
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moat behind him, whether he is technically out of power ask what he will do next. after a few days when nobody said they knew where he was, the president/former president of ukraine is reportedly safe and sound in russia. the russian government said they've offered him protection. from russia today he released a statement arguing that he is still the legitimate president of ukraine. the protesters that filled the camden last week would disagree with that as would the ukrainian parliament which has a new acting president to replace him which is not the sort of thing do you if you think the old guy is still around. in the black sea region, you can see it there in that peninsula area, it has a russian speaking majority, masked gunmen stormed, barricade themselves inside. there they've raised the russian flag. early this morning local police officers sealed off access to government buildings.
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this was the scene during the day. they took down the ukrainian flag chanting, russia, russia. they're calling for their russia oriented side of ukraine to be made part of russia by splitting off from the rest of the country. and this is all happening a day after russian president vladimir putin ordered surprise military exercises for 150,000 troops in russia including some bases quite near the russian ukrainian bored he. these exercises they say are men to test the combat readiness of troops. they are largest ground exercises the russians have done in years. in terms of precedent, in 2008 russia launched exercises of just this sort.
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and that was just a few days before russia went to war with neighboring georgia. so nobody quite knows what will happen here. nobody quite knows what russia will do. and it is not like one thing hams at a time. [ mom ] over the years, i've learned how to stretch my party budget. but when my so-called bargain brand towel made a mess of things, i switched to bounty basic. look! one sheet of bounty basic is 50% stronger than a full sheet of the bargain brand. bounty basic. the strong but affordable picker upper.
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find it at the pharmacy counter. on the advice of my counsel, i assert my right to remain silent under the united states and new jersey constitutions. >> that was port authority david wildstein. the guy who created all of the notorious traffic problems in ft. lee, new jersey. in january he clammed about this whole issue under oath. he invoke the fifth amendment. that earned him a misdemeanor contempt rap in new jersey. now he is maybe the central player in this whole thing. who is the most cooperative with the ongoing investigation. that turns out to be a really important combination of events and those details are straight ahead.
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today these new partially redacted documents related to the new jersey bridge scandal were released to the public. why did we just get them? we have these documents a few weeks ago. but it is today that we're being allowed to see more of what was under the black magic marker lines that x'd out before. on december 6th, david wildstein announced he was resigning as the director of interstate camden projects. don't worry that the job title seems meaningless. it is.
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it is the sweet job which paid him $150,000 a year with you the job had no job description. he had no official responsibility. a few days after he resigned that sweet job, he was subpoenaed by the legislature for documents related to the lane closures on the george washington bridge which he had arranged. he was asked to appear before the legislate our january 9th to answer questions and to turn over those documents. did he show up on january 9th. he brought the documents with him. but he wouldn't answer questions. he invoegd the fifth amendment and refused to answer anything they asked him. he wonltd even confirm that he had previously been employed by the port authority.
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david wildstein was held in contempt that day for refusing to answer the questions but did he handled over about 900 pages of documents. of those 900 pages about, 40 pages were partially or fully redacted with a black magic marker. his lawyer said those redakss were things that weren't relevant to what they were at. but they said they would seek access to the unredacted versions of the documents and get them somehow. and here's where it really gets interesting. a couple weeks after they held david wildstein in contempt which means he will have defend herself against a criminal prosecution for being in contempt of the legislature, a couple weeks after that contempt ruling, his former employer said they would not be paying his legal bills. david wildstein would be on his own. the only thing bing david wildstein's financial situation is that he's been paid $150,000 a year. so he is not a poor person but even if he is very well off it has to be a little daunting to know you're not only being blamed for this scandal and the governor you've known since you were teenager and whom you've served loyally for years is denying he never really knew you. and not only are you being categorized as a rogue operator and you've lost your job because of it but now you'll handle the legal bills for all of it
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yourself? it has to be daunting to know that one of the consequences you're facing here, which no one else is facing is you potentially being personally bankrupted because of your role in this scheme. which after all you didn't do alone. well, after the port authority said they were cutting david wildstein loose and making him pay for his own defense, mr. wildstein sent a letter pleading his case arguing against their decision to not fund his legal fees. that same letter was the one that says evidence exists trying governor chris christie to having knowledge of the lane closures. if there was any george washington bridge left to burn between governor chris christie and david wildstein at that point, i think with that letter
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you can consider that bridge burned. and now this. now these unredacted documents. the unredactions that david wildstein that carefully senseored. what's going on here? earlier this month dade wildstein's lawyer met with a special council whose who have is advising the legislature. they sat down with david wildstein's documents and they negotiated line by line, redaction by redaction, what could legitimately be excluded from the public record because it really was not relevant and what information should be released. they worked together line by line to produce the documents that we got today including the new information that was previously secret information that they decided together now is relevant to the investigation that we didn't know before. and that means that david wildstein is taking at left a baby steps toward assisting the investigation. at least kind of. two of the other people who lost
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their jobs in the scandal are doing the opposite. they err not only not helping they're not handing over documents. now they're trying to argue to a judge that the fifth amendment protects them from having to hand over any documents. but their former colleague, david wildstein, he's the one who really got hung out to dry. not just fired, but blamed and denied and attacked by the governor he was so loyal to. and now potentially bankrupted in the process. david wildstein obviously knows what happened in this scandal. he organized the lane closures in the first place. he knows what happened. he knows why it happened. and presumably, he knows everyone who knew about it and when they knew. has david wildstein now moved from being part of the traffic perpetrator's defense to being on the other side? to being on the side of the folks who are investigating this bridge scandal? and if he has split from the defense to the other side, what does that mean for the future of this story? joining us now is a senior political reporter from the new jersey news website. thank you for being here. >> thanks for having me. so david wildstein and his
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attorney basically gave themselves up to the special counsel to show all of the unredacted documents and this is the product of it. will this help him in some way? >> you know, it might. i think you may have hit it on the head in the opening. he may have been forced to turn this stuff over anyway. eventually if this committee was going to take him to court to try to get those unredacted documents, some judge was going to do what they did, i assume, go through line by line and see what should remain redacted and what had no bearing or what needed to be turned over. i think he just saved himself, you know, i'm sure you guys went through this as well. there's no smoking gun certainly in what was turned over. it makes david look bad, but david looked bad anyway. the whole, making fun of the rabbi and all that stuff. you know, his reputation is not coming back from this anyway, so i'm not sure that really hurts him. it saves him having to go to court to fight this bat physical they try to get these redacted documents turned over.
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so i think it's just easier for him to go through. they turned over, who knows, maybe on the other side of it, there was some gray area and the committee said we can go without that one. who knows what the negotiation looked like. but i think you're right, i think it probably just saved h him some headaches. maybe it buys him a little good will, maybe it wasn't. but it saves him some grief. >> the reason he seems so important here is that he knows everything. if you had to pick one person who you wanted to get the real story from, this is the guy you would pick. he knows what bridget kelly was talking about when he says time for traffic problems in ft. lee. you know from going way back in new jersey journalism and new jersey politics, if he has been scorned by chris christie, scorned by the people who previously, he worked with. and sort of blamed for this thing to, and potentially bankrupted for this thing, is he the kind of guy who would hold a grudge? lash out?
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>> i don't know that side of him, but yeah, i think so. and the speculation we've all sort of had is when christie threw him under the bus with the, i was an athlete and the president of the student class. i don't think what dave david was doing in those days. we were at different lunch tables. that had to sting a little bit, considering he was taking the fall for this thing that i'm sure he thought he was doing on behalf of the governor. so i wouldn't be surprised if that's part of it. if there's something going on here where he's saying i'm not going down alone for this. there were other people out there. >> i'm going to go down i'm going to make sure i make it worth -- >> when he first turned over the initial documents, it was like a loaded gun aimed at ten different people in the city of trenton. there were random one-offs that maybe they needed to be in there, maybe they didn't. but they were in there. >> he seems like a guy who at least know what is he's doing, even if we're not quite sure yet. thank you for being here. appreciate it. >> coming up, the arizona effect
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in the deep south of all places. that story is next. >> i have not heard one example in arizona where a business owner's religious liberty has been violated. the bill is broadly worded and could result in unintended and negative consequences. after weighing all of the arguments i have vetoed senate bill 1062 moments ago. what if you could shrink your pores just by washing your face? [ female announcer ] neutrogena® pore refining cleanser. alpha-hydroxy and exfoliating beads work to clean and tighten pores so they can look half their size. pores...shrink 'em down to size! [ female announcer ] pore refining cleanser. neutrogena®. are you flo? yes. is this the thing you gave my husband? well, yeah, yes. the "name your price" tool. you tell us the price you want to pay, and we give you a range of options to choose from. careful, though -- that kind of power can go to your head. that explains a lot. yo, buddy! i got this. gimme one, gimme one, gimme one! the power of the "name your price" tool.
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she'd just grab the bounty select-a-size. one select-a-size sheet of bounty is 50% more absorbent than a full size sheet of the leading ordinary brand. use less, with bounty select-a-size. >> i have not heard one example in arizona where a business owner's religious liberty has been violated. the bill is broadly worded and could result in unintended and negative consequences. after weighing all of the arguments i have vetoed senate bill 1062 moments ago. >> the immediate impact of arizona governor january brewer's decision to veto arizona senate bill 1062 last night was celebrations.
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arizona will not have a new business climate where your gay friends and your straight friends may be cannot eat at the same restaurant. and so people waved flags and people celebrated and the social conservatives on the right got very mad on twitter. but while that was happening last night concerning arizona, at almost the exact same time, this was happening in the great state of georgia. >> just into our news room within the past 15 minutes, after backlash from gay and lesbian groups the so-called religious bill appears dead for this year's legislative session. members and supporters of the gay and lesbian community packed into a hearing this week at the state capitol. a senate committee has just pulled that bill from the floor. as arizona governor jan brewer was deciding to kill her state's religious freedom/discrimination bill last night, georgia republicans decided to kill theirs as well. and earlier this week, you may remember, we told you about a similar bill that had popped up in mississippi. that bill was passed unanimously by the state senate in
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mississippi and was headed towards the mississippi house but then last night, a house subcommittee in mississippi decided to gut that bill as well. they removed the you are free to discriminate parts of it and left only the part of the bill that would put in god we trust into the mississippi state seal. we've now seen similar religious freedom/discrimination bills tabled or vetoed or deferred or withdrawn inform kansas and tennessee and ohio and utah and idaho and oklahoma and now arizona and now georgia and now mississippi. nine states. nine state governments have been evaluating this type of legislation. and in a number of them, it was racing through republican-dominated legislatures. but in nine state, all of a sudden, just in the last few days and in some cases just in the last few hours those states have either decided it's playing a little too fast and loose with civil liberties or more likely
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they saw the firestorm that was unleashed in arizona and decided no thanks, we don't really want to deal with that. it's never over till it's over for things like this. for this experiment in first look is up next. good friday morning, everybody. right now on "first look" vakzs ordered. massive rains and strong winds in california today prompting fears of flooding, landslides, and other damage to the drought-ridden state. tensions rising. reports of russian military aircraft replying over ukraine while ousted president reports surface. oscar fever. the academy awards. let's move obama and bide erj on the run in the white house. plus, lebron james returns to the hard court. jeff gordon's latest prank may be his best yet. and the statue
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WASHINGTON Information about debt sold in the $2.8 trillion U.S. municipal bond market is frequently not made available to investors, or is posted well after it could have any meaning for those buying bonds, according to a study released on Thursday by a leading municipal data provider.
Using a large sampling of issuers, the study by DPC Data, Inc. found that the time it took to file financial statements had lengthened as concerns about states and local governments' fiscal health grew. That means that when investors are demanding more information, less is becoming available.
"Clearly, there is a pervasive laxness in filing that obstructs the dependable and timely flow of meaningful financial information to investors and those that serve their interests," the study found.
While concerns about disclosure are not new, they are growing as states and local governments struggle to recover from the economic recession that began in 2007 and caused a collapse in their revenues.
"The dangers for investors is implicit when disclosure behavior worsens while underlying economic and financial stresses mount, as is the case now," DPC said.
The survey found that prior to the economic recession, in the years 2005 to 2007, one-third of issuers required to make disclosures failed to do so. But during the recession years of 2008 and 2009, that proportion rose to 36 percent and 40 percent, respectively.
DPC surveyed 18,913 new municipal issues during 2008, 2009 and 2010 and found that each year issuers had increased the window of time they pledged to make their financial statements available in their disclosure covenants.
On a positive note, DPC could not find a correlation between disclosure and default, meaning that those who were most negligent in posting information had no greater risk of default than those who submitted information in a timely way.
Still, it said, investors who are told about the threat of default have "the best defense against surprises."
Spooked by the possibility of defaults and pervasive fiscal problems, municipal bond investors have fled the market in recent months.
"Because of this economic climate and the fragile financial circumstances it has wrought for many entities, access to disclosure documents has never been more important," DPC said.
For those who did file financial statements, 5 percent of issuers took more than one year after the close of their fiscal year to make the financial statements available, and DPC said the trend for most issuers is to file statements more than 180 days after the end of their fiscal years.
For many years, state and local government debt issuers sent their disclosures to DPC and other companies, which then provided the information for a fee.
Following concerns from federal regulators and municipal bond buyers about transparency, the Municipal Securities Rulemaking Board began to provide information for free in 2008 on its website. The board, which writes the rules for the market that the Securities Exchange Commission enforces, also began requiring issuers to file more comprehensive data in a timely manner.
DPC said it used its expertise to study the MSRB's data. (Editing by Leslie Adler)
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Complements: Red-Orange and Blue-Green
The two colors you choose don't have to have equal prominence in the room to work. You can use one as the main color and the other as an accent, or bring small colored accessories into an already painted room to see how you feel about the pairing. Here, the energetically bright orange-red towel and glass pop against the cool, blue-green walls without overwhelming the soothing hue.


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The SEC's Division of Investment Management issued updated guidance regarding the definition of "knowledgeable employees" under Rule 3c-5 of the Investment Company Act of 1940. See Managed Funds Ass'n, SEC No-Action Letter, avail. 2/6/14, https://www.managedfunds.org/wp-content/uploads/2014/02/Staff-Response-to-MFA-3c-5-Letter-Final-Outgoing-2-6-14-no-sigs.pdf The SEC staff explained that "private funds" include private equity funds, hedge funds, and other pooled investment vehicles, excluded from… Continue Reading
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Cash Solicitations Allowed Despite Court BarPosted in Hedge and Private Equity Funds, Investment Adviser Regulation
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FINRA and Alt Funds RisksPosted in FINRA Compliance, FINRA Enforcement, Hedge and Private Equity Funds
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Patent application title: System and Method for Unsupervised and Active Learning for Automatic Speech Recognition
Dilek Zeynep Hakkani-Tur (Morris Plains, NJ, US)
Giuseppe Riccardi (Hoboken, NJ, US)
Giuseppe Riccardi (Hoboken, NJ, US)
IPC8 Class: AG10L1526FI
USPC Class:
Class name: Speech signal processing recognition speech to image
Publication date: 2009-08-06
Patent application number: 20090198493
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A system and method is provided for combining active and unsupervised
learning for automatic speech recognition. This process enables a
reduction in the amount of human supervision required for training
acoustic and language models and an increase in the performance given the
transcribed and un-transcribed data.
1. A method of training an automatic speech recognition module, the method
comprising:(1) training acoustic and language models using on set of
transcribed data and speech recognition and word confidence scores for a
retrieved set of un-transcribed data; and(2) iteratively performing step
(1) if word accuracy has not converged.
2. The method of claim 1, the method further comprising:(3) prior to the training of step (1), training the acoustic and language models using another set of transcribed data.
3. The method of claim 2, further comprising:(4) recognizing utterances in a set of candidates for transcription using the acoustic and language models of step (3).
4. The method of claim 3, further comprising:(5) computing by a processor confidence scores of the utterances.
5. The method of claim 4, further comprising:(6) selecting k utterances that have the smallest confidence scores from the set of candidates and transcribing them into a first additional transcribed set.
6. The method of claim 5, further comprising:(7) adding the first additional transcribed set to the another set of transcribed data to produce the set of transcribed data used in step (1).
7. The method of claim 6, further comprising:(8) removing the first additional transcribed set from the set of candidates.
8. The method of claim 7, wherein the retrieved set of un-transcribed data is retrieved from the set of candidates.
9. The method of claim 5, wherein k is more than one.
10. The method of claim 5, wherein step (6) further comprises leaving out utterances with confidence scores indicating that the utterances were correctly recognized.
11. The method of claim 1, wherein word posterior probability estimates are used for word confidence scores associated with the utterances.
12. The method of claim 1, wherein a word is considered to be correctly recognized if it has a confidence score higher than a threshold value.
13. The method of claim 8, wherein step (9) further comprises selecting a sample of un-transcribed data.
14. A tangible computer-readable medium that stores a program for controlling a computer device to perform a method to train an automatic speech recognition module, the method comprising:(1) training acoustic and language models using on set of transcribed data and speech recognition and word confidence scores for a retrieved set of un-transcribed data; and(2) iteratively performing step (1) if word accuracy has not converged.
15. The tangible computer-readable medium of claim 14, wherein the method further comprises, prior to the training of step (1), (3) training the acoustic and language models using another set of transcribed data, (4) recognizing utterances in a set of candidates for transcription using the acoustic language models and (6) computing by a processor confidence scores of the utterances.
16. The tangible computer-readable medium of claim 15, wherein the method further comprises (6) selecting k utterances that have the smallest confidence scores from the set of candidates and transcribing them into a first additional transcribed set, (7) adding the first additional transcribed set to the another set of transcribed data to produce the set of transcribed data used in step (1) and (8) removing the first additional transcribed set from the set of candidates and wherein the retrieved set of un-transcribed data is retrieved from the set of candidates.
17. A spoken dialog system, the system comprising:an automatic-speech recognition module trained using a method of training an automatic-speech recognition module and stored in a memory storage device, the method comprising:(1) training acoustic and language models using on set of transcribed data and speech recognition and word confidence scores for a retrieved set of un-transcribed data; and(2) iteratively performing step (1) if word accuracy has not converged.
18. The spoken dialog system of claim 17, wherein the method further comprises, prior to the training of step (1), (3) training the acoustic and language models using another set of transcribed data, (4) recognizing utterances in a set of candidates for transcription using the acoustic language models and (6) computing by a processor confidence scores of the utterances.
19. The spoken dialog system of claim 18, wherein the method further comprises (6) selecting k utterances that have the smallest confidence scores from the set of candidates and transcribing them into a first additional transcribed set, (7) adding the first additional transcribed set to the another set of transcribed data to produce the set of transcribed data used in step (1) and (8) removing the first additional transcribed set from the set of candidates and wherein the retrieved set of un-transcribed data is retrieved from the set of candidates.
[0001]This application is a continuation of U.S. patent application Ser. No. 10/742,854, filed Dec. 23, 2003, the contents of which is incorporated herein by reference in its entirety.
[0002]1. Field of the Invention
[0003]The present invention relates generally to speech recognition systems and, more particularly, to an unsupervised and active learning process for automatic speech recognition systems.
[0004]2. Introduction
[0005]State-of-the-art speech recognition systems require transcribed utterances for training of various knowledge sources, such as language and acoustic models, used in the speech recognition system. In general, acoustic models can include the representation of knowledge about acoustics, phonetics, microphone and environment variability, gender and dialect differences among speakers, etc. Language models, on the other hand, can refer to a system's knowledge of what constitutes a possible word, what words are likely to co-occur, and in what sequence. The semantics and functions related to an operation a user may wish to perform may also be necessary for the language model.
[0006]Many uncertainties exist in automatic speech recognition (ASR). For example, uncertainties may relate to speaker characteristics, speech style and rate, recognition of basic speech segments, possible words, likely words, unknown words, grammatical variation, noise interference, and normative accents. Each of these uncertainties can be the cause in the reduction of recognition success in an ASR system. A successful speech recognition system must therefore contend with all of these issues.
[0007]A speech recognition system generally seeks to minimize uncertainties through the effective training of the system. As noted, this training is based on the generation of transcribed utterances, a process that is labor intensive and time-consuming process. As would be appreciated, if issues of cost were ignored, more effective speech recognition systems could be created through the use of greater amounts of transcribed data. This does not represent a practical solution. What is needed therefore is an efficient mechanism for creating a quality speech recognition system using all available sources of training data, whether existing in transcribed or un-transcribed form.
[0008]In accordance with the present invention, a process is provided for combining active and unsupervised learning for automatic speech recognition. This process enables a reduction in the amount of human supervision required for training acoustic and language models and an increase in the performance given the transcribed and un-transcribed data.
[0009]Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The features and advantages of the invention may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the present invention will become more fully apparent from the following description and appended claims, or may be learned by the practice of the invention as set forth herein.
[0010]In order to describe the manner in which the above-recited and other advantages and features of the invention can be obtained, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only typical embodiments of the invention and are not therefore to be considered to be limiting of its scope, the invention will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:
[0011]FIG. 1 illustrates an exemplary method of the present invention;
[0012]FIG. 2 illustrates a scatter plot of n-gram probabilities estimated from clean vs. noisy speech utterance transcriptions;
[0013]FIG. 3 illustrates the false rejection rate versus false acceptance rate;
[0014]FIG. 4 illustrates the percentage of correctly recognized words in confidence score bins;
[0015]FIG. 5 illustrates the percentage of correctly recognized words in confidence score bins;
[0016]FIG. 6 illustrates the vocabulary size growth with random and selective sampling; and
[0017]FIG. 7 illustrates word accuracy learning curves.
[0018]Various embodiments of the invention are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations may be used without parting from the spirit and scope of the invention.
[0019]As noted, state-of-the-art automatic speech recognition (ASR) systems require transcribed utterances for training. Utterances may be obtained from recorded conversations between customers and a call center. A developer or a team of developers must transcribe and label the utterances to identify user intent. Not every utterance is valuable, however. For example, if a user calls a call center to order a television, he may talk with the call center employee about the weather before saying "I'd like to order a TV."
[0020]The transcription process used to generate these transcribed utterances is labor intensive and time-consuming. One method of reducing the number of training examples to be labeled is through active learning, which inspects the unlabeled examples, and selectively samples the most informative ones with respect to a given cost function for a human to label. One of the goals of the active learning algorithm is to select the examples for labeling that will have the largest performance improvement. Another method of reducing the cost of training an ASR system is through unsupervised learning, which aims at utilizing unlabeled examples, to train, augment, or adapt the system.
[0021]In accordance with the present invention, the transcription effort for training an ASR system is reduced through the combination of active and unsupervised learning techniques. As will be described, this process uses word and utterance confidence scores for utterance selection in active learning, and probability estimation in unsupervised learning. In the description following below, the main active and unsupervised learning processes are briefly described first, followed by a description of their combination.
[0022]Active learning is generally designed to aid and automate the labor-intensive process of building and training speech recognition models. One of the goals of an active learning system is to significantly reduce the amount of transcribed data required to train ASR models with a given level of accuracy. This reduction in transcribed data will ultimately reduce the cost and time-to-market for natural language services.
[0023]In the active learning process, a speech recognizer is first trained using a small set of transcribed data St. This small set may be manually generated from a group of utterances or automatically generated from web pages related to the application domain. Using the recognizer, the system recognizes the utterances that are candidates for transcription Su, where Su is an additional un-transcribed set. Using lattice-based confidence measures, the system predicts which candidates are recognized incorrectly. The human transcriber then transcribes the utterances that are most likely to have recognition errors.
[0024]In unsupervised learning, one of the goals is exploiting un-transcribed data to either bootstrap a language model or in general improve upon the model trained from the transcribed set of training examples. In other words, the model trained from the transcribed set of training examples is augmented using machine-labeled data rather than human-labeled data.
[0025]FIG. 1 illustrates an embodiment of a combined unsupervised and active learning process for automatic speech recognition. As illustrated, the process begins at step 102 through the training of the initial acoustic and language models AM' and LM' for recognition using a small set of transcribed data St and also possibly a set of available un-transcribed utterances. Here, the variable "i" is the iteration number, and as shown in step 102, i=0 for the initially trained acoustic and language models AM' and LM'. Next, at step 104, the utterances in set Su are recognized using the trained acoustic and language models. At step 106, the confidence scores are computed for all of the words or un-transcribed utterances. Before continuing with the flow chart of FIG. 1, the calculation of confidence scores will be discussed.
[0026]Confidence scores can be calculated in various ways. In a first embodiment, confidence scores are calculated based on acoustic measurements. In a second embodiment, confidence scores are calculated based on word lattices. The second embodiment has the advantage that the probability computation does not require training of an estimator. As would be appreciated, hybrid approaches that use features of these two embodiments can also be used.
[0027]In accordance with the present invention, word confidence scores are extracted from the lattice output of the ASR system. In general, the word confidence score extraction process comprises: computing the posterior probabilities for all transitions in the lattice; extracting a path from the lattice (which can be the best, longest or a random path, which path is called the pivot of the alignment); traversing the lattice; aligning all the transitions with the pivot; and merging the transitions that correspond to the same word (or label) that occur in the same interval (by summing their posterior probabilities). A detailed explanation of this process and the comparison of its performance with other approaches is presented in Hakkani-Tur et al., A General Algorithm for Word Graph Matrix Decomposition, Proceedings of ICASSP, Hong Kong, April 2003, which is incorporated by reference herein in its entirety.
[0028]The state times or approximate state locations on the lattice are used to align transitions that have occurred at around the same time interval. The final structure is called a pivot alignment. The word posterior probability estimates on the best path of the pivot alignments are used as word confidence scores, c,.
[0029]For active learning, different approaches were evaluated to obtain utterance level confidence measures from word confidence scores that are extracted from ASR lattices. One approach is to compute the confidence score of an utterance as the arithmetic mean of the confidence scores of the words that it contains:
c , , = I n n lc ; ( Equation 1 ) ##EQU00001##
[0030]Another approach is to compute the confidence score of an utterance as the product of the confidence scores of the words that it contains:
c,,=Ica'.sup.('''.sup.) (Equation 2)
where a,(14),) is a scaling function. Other functions can also be used to compute the utterance confidence scores:
c.sub.w,,,=F(c,, . . . , cn) (Equation 3)
where F can be the geometric mean or the min function.
[0031]Returning to the flowchart of FIG. 1, after the confidence scores are computed at step 106, k utterances are selected, at step 108, that have the smallest confidence scores from Su. Next, at step 110, the k selected utterances are transcribed by human effort. At step 112, the new k selected utterances that are transcribed and denoted by Sk, are added to the transcribed set St (i.e., S,I+1=S:U Ski), while also being subtracted from the set of available utterances Su (i.e., S'71=Su1-Ski). The new set of training data S;+1 can then be used to train the new acoustic and language models AM'.sup.±1 and LIV11+1 at step 114.
[0032]As illustrated, the additional un-transcribed set Su is made available to the ASR system processing at steps 104 and 112. The initial transcribed set St is also made available to the initial training step 102, recalculation step 112, and training step 114.
[0033]At step 116, a determination is also made as to whether the word accuracy (WER=word error rate) has converged. If it is determined that the word accuracy has not converged, then the process loops back to step 104. If it is determined that the word accuracy has converged, then the process ends as further transcription is not expected to improve ASR performance. The process can be repeated again later, as new un-transcribed data from the domain becomes available.
[0034]As further illustrated in FIG. 1, un-transcribed data Su can also be used to train the acoustic and language models AMI+1 and LM'.sup.±1 at step 114. More specifically, the ASR output and word confidence scores for un-transcribed data are used to train, augment, or adapt the ASR system. As illustrated in FIG. 1, in one embodiment, only a selected sample (chosen at step 118) of un-transcribed data are used to train, augment, or adapt the ASR system. This element of unsupervised learning is therefore used in combination with the active learning process described above.
[0035]One issue with unsupervised learning is the estimation of the error signal. In the case of language modeling the error signal is the noise on the event counts. Even in the simple case of n-gram language modeling the n-gram counts in the presence of noise are very unstable. FIG. 2 illustrates a scatter a plot of n-gram probabilities as estimated from clean (true) transcriptions of speech (x axis) and estimated from noisy transcriptions (y axis), in this case the ASR output. As illustrated, FIG. 2 shows how the error variance increases for infrequent n-grams.
[0036]In standard n-gram estimation the occurrences of n-tuples C(win) are counted, where win is the word n-tuple wi, W23 . . . /wn. In unsupervised learning the nature of the information is noisy and n-gram counts are estimated from two synchronized information channels, the speech utterance hypothesis and the synchronized error signal. For each word w,, the probability of being correctly decoded c, (=1-e,, where e, is the error probability) is estimated, resulting in the confidence score. The bidimensional channel is then represented as a sequence of n-tuples of symbol pairs (win,cin)=(wi,c 1)(w2,c2), . . . , (wn,cn). The n-gram counts in presence of noise can be computed by marginalizing the joint channel counts:
CuL ( w ; ' ) = c x 6 , 4 , ( x ) xET ( Equation 4 ) ##EQU00002##
where c,, is the confidence score for the n-tuple x in the noisy spoken utterance transcriptions T and 6,,(x) is the indicator function for the n-tuple win. The confidence score of the n-tuple win can be computed by geometric or arithmetic means or max and min over the n-tuple of word confidence scores c,''. In one embodiment, the simplest approach is taken where cv4,=c12 is computed. Equation 4 can be rewritten as a function of the error probability en:
C UL ( 1 i W ) = C ( i n W ) --0 4 ( x ) xET ( Equation 5 ) ##EQU00003##
[0037]Equation 5 shows the relation between the count estimates with and without error signal CuL(w,'') and C(win), respectively.
[0038]A series of experiments verified that word confidence scores can be used to identify correctly recognized words, utterance confidence scores can be used to select more informative utterances to transcribe, and automatic speech recognition accuracy can be improved by exploiting un-transcribed data. For all these experiments, utterances from AT&T's How May I Help You?SM (HMIHY) speech database was used. See A. Gorin et al., "Semantic Information Processing of Spoken Language, In Proc. of ATR Workshop on Multi-Lingual Speech Communication. 2000, which is incorporated herein by reference in its entirety. The language models used in all experiments were trigram models based on Variable Ngram Stochastic Automata. See G. Riccardi et al., "Stochastic Automata for Language Modeling," Computer Speech and Language, 10:265-293, 1996, which is incorporated herein by reference in its entirety.
[0039]In the HMIHY speech database there were two distinct data collections. The first is from human-human conversations (8K utterances and 300K word tokens) and consists of responses to the initial prompt. The second is from human-machine dialogs (28K and 318K word tokens) and consists of users' responses to all system prompts (e.g. greeting and confirmation prompts). The test data consists of 1,000 utterances (10K words) from the human-machine data collection. All experiments kept the acoustic model fixed, and used a model, which is subword unit based, with triphone context modeling, and trained using utterances from human-human conversations, and other telephone speech. The training data for the acoustic model did not overlap with the additional training data.
[0040]The language model that was used was trained using all the human-human utterances to recognize the test data, and computed word confidence scores for the ASR output. The word accuracy of the test set was 63.8%. To check how good the word confidence scores were in distinguishing the correctly recognized and misrecognized words, a binary classification problem was considered, where the confidence scores of the words was used, as well as a threshold for the decision. Each word was classified as correctly recognized if that word had a confidence score higher than the threshold, and as misrecognized otherwise. False rejection and false acceptance rates were computed by changing the threshold and comparing the classifications with the references (see FIG. 3). False rejection rate is the percentage of correctly recognized words that are classified as misrecognition, since their confidence score is lower than the threshold. False acceptance rate is the percentage of misrecognized words that are accepted as correct recognitions, since their confidence score is higher than the threshold. On the test set, the equal error rate (False Rejection Rate equals False Acceptance Rate) is 22%.
[0041]Alternatively, FIG. 4 also plots the percentage of correctly recognized words in confidence score bins. As illustrated, for example, 8.1% of the words that have a confidence score in the interval [0.1,0.2] are correct recognitions, and the rest are misrecognitions. One may expect k % of the words having the confidence score of k/100 to be correct. As seen, the percentage of correctly recognized words in each confidence score bin increases almost linearly as the confidence score increases.
[0042]Utterance confidence scores are computed from word confidence scores for selectively sampling the utterances. FIG. 5 shows a scatter plot of utterance confidence score versus the percentage of correctly recognized words in that utterance. As illustrated, as the confidence score of the utterances increase, the percentage of correctly recognized words also increase, showing that good quality utterance confidence scores can be computed using word confidence scores.
[0043]To show the effectiveness of the estimates of the noisy counts n-gram language models were trained to run large vocabulary speech recognition experiments. A baseline stochastic language model (SLM) was trained from 1K clean human-human speech transcriptions (baseline) and then augmented with 20K noisy transcriptions with and without error signal (ASR output and Confidence Scores). Table 1 shows the Word Accuracy (WA) results on 1K test set taken from the HMIHY database. Using the counts estimated using equation 5 a 30% reduction of the gap between the baseline and supervised SLM (Upper bound) was achieved without requiring further transcription of speech utterances.
TABLE-US-00001 TABLE 1 Training Set WA Baseline 59.1% ASR output + baseline 61.5% ASR output + Confidence Scores + baseline 62.1% Upper bound 69.4%
[0044]For active learning in ASR, an initial language model was trained using the initial set utterances from human-human interactions. Using this model, lattices and pivot alignments were then generated for the additional training data, and the confidence scores for words and utterances were then computed. Using the confidence scores for utterances, the utterances were then sorted in an order according to "usefulness" for ASR (generating the selectively sampled order). The language models were incrementally trained by using the top n utterances from the randomly sampled and selectively sampled orders, and learning curves were generated for word accuracy and vocabulary size (See FIGS. 6 and 7). The experiments used the arithmetic mean of the word confidence scores (i.e., F is the mean function in equation 3) as utterance confidence scores, which gave the best results. The active learning process is independent of the way the confidence scores are computed. In the experiments, the normalized utterance likelihood was also used as a sampling criterion, and it gave inferior performance.
[0045]FIG. 7 also shows a learning curve when active and unsupervised learning are combined. For this curve, transcriptions of the top n utterances were added to the selectively sampled order, and the automatic speech recognizer output of the rest of the utterances to the training set for the language models. The recognizer output was generated using the initial language model, and contained word confidence scores. FIG. 7 shows the word accuracy learning curves when additional un-transcribed data was exploited by combining active learning with unsupervised learning.
[0046]These curves illustrate that active learning is effective in reducing the need for labeled data (for a given word accuracy). For example, to achieve 66.5% word accuracy with random sampling, transcriptions for 4,000 utterances were needed, however, this accuracy can be achieved by transcribing only around 2,500 utterances. This shows that the same performance is achieved by transcribing 38% fewer utterances, when active learning is used. In addition, a huge improvement is obtained using the un-transcribed data, at the very initial points. For example, the combination of active and unsupervised learning produces a 66.5% word accuracy by using only 1,000 transcribed utterances instead of 4,000 transcribed utterances, that is 75% less utterances than random sampling. As more transcribed data is produced, the improvement using un-transcribed data gets less and active learning takes over. The combination always results in higher word accuracy than random sampling, by 2-3% points.
[0047]As thus described, new methods are provided for reducing the amount of labeled training examples by selectively sampling the most informative subset of data for transcription using lattice based confidence measures, and exploiting the rest of the data, that has not been transcribed, by using the ASR output and word confidence scores. By selective sampling using utterance-level confidence measures, the same word accuracy results are achieved using 38% less data. It has been shown that it is possible to detect utterances that have little new information when added to an initial set of utterances. In addition to this, it has been shown that it is possible to exploit the un-transcribed data, and the same word accuracy results can be achieved using 75% less data by combining active and unsupervised learning.
[0048]Using the principles of the present invention, an improved process is provided for creating an ASR module. Thus, the present invention envisions a further embodiment that includes a spoken dialog service having an ASR module generated by the principles of the present invention.
[0049]Embodiments within the scope of the present invention may also include computer-readable media for carrying or having computer-executable instructions or data structures stored thereon. Such computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code means in the form of computer-executable instructions or data structures.
[0050]Computer-executable instructions include, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Computer executable instructions also include program modules that are executed by computers in stand-alone or network environments. Generally, program modules include routines, programs, objects, components, and data structures, etc. that perform particular tasks or implement particular abstract data types. Computer-executable instructions, associated data structures, and program modules represent examples of the program code means for executing steps of the methods disclosed herein. The particular sequence of such executable instructions or associated data structures represents examples of corresponding acts for implementing the functions described in such steps.
[0051]Those of skill in the art will appreciate that other embodiments of the invention may be practiced in network computing environments with many types of computer system configurations, including personal computers, hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers, and the like. Embodiments may also be practiced in distributed computing environments where tasks are performed by local and remote processing devices that are linked (either by hardwired links, wireless links, or by a combination thereof) through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
[0052]Although the above description may contain specific details, they should not be construed as limiting the claims in any way. Other configurations of the described embodiments of the invention are part of the scope of this invention. For example, the preferred embodiments of the invention may be described with reference to ASR components within a spoken dialog system. However, the invention may have applicability in a variety of environments where ASR may be used. Therefore, the invention is not limited to ASR within any particular application. Accordingly, the appended claims and their legal equivalents only should define the invention, rather than any specific examples given.
Patent applications by Dilek Zeynep Hakkani-Tur, Morris Plains, NJ US
Patent applications by Giuseppe Riccardi, Hoboken, NJ US
Patent applications by AT&T CORP.
Patent applications in class Speech to image
Patent applications in all subclasses Speech to image
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