New Results with the Lincoln Tied-Mixture HMM CSR System

New Results with the Lincoln Tied-Mixture HMM CSR System 1 Douglas B. Paul Lincoln Laboratory, MIT Lexington, Ma. 02173 ABSTRACT The following describes recent work on the Lincoln CSR system. Some new variations in semiphone modeling have been tested. A very simple improved duration model has reduced the error rate by about 10~ in both triphone and semiphone systems. A new training strategy has been tested which, by itself, did not provide useful improvements but suggests that improvements can be obtained by a related rapid adaptation technique. Finally, the recognizer has been modified to use bigram back-off language models. The sys- tem was then transferred from the RM task to the ATIS CSR task and a limited number of development tests per- formed. Evaluation test results are presented for both the RM and ATIS CSR tasks. INTRODUCTION The following experiments are all carried out in the context of the Lincoln tied-mixture (TM) hidden Markov model (HMM) continuous speech recognition (CSR) sys- tem. This system uses two observation streams (TM-2) for speaker-dependent (SD) recognition: mel-cepstra and time differential mel-cepstra. For speaker-independent (SI) recognition, a second differential mel-cepstral observation stream is added (TM-3). The system uses Gaussian tied mixture [1, 2] observation pdfs and treats each observation stream as if it is statistically independent of all others. Tri- phone models [14], including cross-word triphone models [10, 7, 16], are used to model phonetic coarticulation. These models are smoothed with reduced context phone models [14]. Each phone model is a three state "linear" (no skip transitions) HMM. The phone models are trained by the forward-backward algorithm using an unsupervised mono- phone (context independent phone) bootstrapping proce- dure. The recognizer extrapolates (estimates) untrained phone models and recognizes using a Viterbi beam search. The initial implementation uses finite-state grammars, con- tains an adaptive background model, and allows optional inter-word silences. All RM1 development tests use the des- ignated SD development test set (100 sentences x 12 speak- 1This work was sponsored by the Defense Advanced Research Projects Agency. ers) and all RM2 tests use the designated development test set (120 sentences x 4 speakers). SEMIPHONES One difficulty with the current triphone-based HMM systems with cross-word triphone models is that the num- ber of triphones becomes very large (~60K triphones) when used in a large (20K word) vocabulary task[ll]. This re- quires estimation of very large numbers of parameters and makes execution of the trainer and recognizer inefficient on practical hardware. We have previously proposed semi- phones as a modeling unit because they significantly reduce the number of elemental phonetic models by as much as an order of magnitude. (Semiphone models split each phone into a triplet of left and right context dependent models[11]. Semiphones include triphones and "classic" diphones--which extend from the center of one phone to the center of the next--as special cases.) On the Resource Management (RM) task, they reduced the number of unique states by about a factor of 5 at the cost of a performance penalty of about 20% for the …

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