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Evaluating the Aurora connected digit recognition task - a bell labs approach

Connected digit recognition has always been an ideal task for fundamental research in speech recognition due to its relatively low complexity and potential applications. In Bell Labs we have developed a number of techniques targeting directly or in-directly at connected digit recognition. For the Aurora task, we study a few such algorithms for the entire spectrum of the issues, including feature extraction, context-dependent digit modeling, minimum classiﬁcation error acoustic modeling, unsupervised noise compensation, and utterance veriﬁcation. We show how each component contributes to the reduction of digit recognition and veriﬁcation errors in adverse conditions. Average over all three test sets we obtained 84.6% and 91.3% digit accuracies for clean- and multi-condition training, respectively. This represents an average of 48.6% error rate reduction when compared to the ofﬁcial Aurora baseline results.