{'fraction_non_alphanumeric': 0.023701002734731084, 'fraction_numerical': 0.0036463081130355514, 'mean_word_length': 5.134078212290503, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Automatic speech recognition for webcasts: how good is good enough and what to do when it isn't

The increased availability of broadband connections has recently led to an increase in the use of Internet broadcasting (webcasting). Most webcasts are archived and accessed numerous times retrospectively. One challenge to skimming and browsing through such archives is the lack of text transcripts of the webcast's audio channel. This paper describes a procedure for prototyping an Automatic Speech Recognition (ASR) system that generates realistic transcripts of any desired Word Error Rate (WER), thus overcoming the drawbacks of both prototype-based and Wizard of Oz simulations. We used such a system in a user study showing that transcripts with WERs less than 25% are acceptable for use in webcast archives. As current ASR systems can only deliver, in realistic conditions, Word Error Rates (WERs) of around 45%, we also describe a solution for reducing the WER of such transcripts by engaging users to collaborate in a "wiki" fashion on editing the imperfect transcripts obtained through ASR.