A data selection strategy for utterance verification in continuous speech recognition
Hui Jiang, Frank K. Soong, Chin‐Hui Lee · 2001
ABSTRACTIn this paper, we propose the concept of rival for verifying hy-pothesis in speech recognition. A likelihood ratio test, based onthe rivals model, are investigated for utterance verification in con-tinuous speech recognition. We present a data selection strategyto identity useful subsets of training data to train rival model auto-matically from training data. And a single pass strategy for utter-ance verification, namely verification-in-search, is also proposed.Some preliminary experiments on DARPA Communicator traveltask have shown the rival models give better verification perfor-mance in terms of identifying mis-recognized words from the out-put of our baseline recognizer.1. INTRODUCTIONRecent advances in automatic speech recognition (ASR) technol-ogy have enabled ASRsystems tomigrate from laboratory to manyservices and products. However, in many practical applications, itbecomes more desirable and urgent to equip a speech recognizerwith utterance verification(UV).[4] Utterance verification isa pro-cedure used to verify how reliable are the results from a speechrecognizer. Usually, a quantitative score, also called confidencemeasure, is used to indicate the reliability of every recognition de-cision. Based on the confidence measure, a series of further ac-tions can be taken after recognition, e.g., to reject or remedy therecognition results. Utterance verification is a crucial technique tomake today’s speech recognizers more “intelligent” than ever be-fore. For instance, a speech recognizer with a powerful UV capa-bility will be able to smartly reject non-speech noises, detect/rejectout-of-vocabulary words, even correct some potential recognitionmistakes, guide the system to perform unsupervised learning, andprovide side information to assist high level speech understanding,etc.Extensivestudies on utterance verification havebeen performedrecently in the literature. One of the most important progressesis to cast utterance verification scenario as a statistical hypothesistesting problem.[4, 7] According to the Neyman-Pearson Lemma,an optimal test is to evaluate a likelihood ratio between two hy-potheses,