Training Set Issues in SRI's DECIPHER Speech Recognition System

SRI has developed the DECIPHER system, a hidden Markov model (HMM) based continuous speech recognition system typically used in a speaker-independent manner. Initially we review the DECIPHER system, then we show that DECIPHER's speakerindependent performance improved by 20% when the standard 3990-sentence speaker-independent test set was augmented with training data from the 7200-sentence resource management speaker-dependent training sentences. We show a further improvement of over 20% when a version of corrective training was implemented. Finally we show improvement using parallel maleand femaletrained models in DECIPHER. The word-error rate when all three improvements were combined was 3.7% on DARPA's February 1989 speaker-independent test set using the standard perplexity 60 wordpair grammar.