Context dependent phonetic duration models for decoding conversational speech

Conversational speech provides a particularly difficult task for speech recognition. It provides much more variability than either dictation, read speech, or isolated commands. Phonetic context was used to predict the durations of phones using a decision tree. These predictions were used to calculate context dependent HMM transition probabilities for these phone models, which were used to decode telephone conversations from the SwitchBoard corpus. We observed that the duration models do not appreciably improve the word error rate; that more can be gained by modeling phone durations within words than by adjusting for local average speaking rates; and conclude that local or global variations in speaking rate are not major contributors to the observed high error rates for SwitchBoard