A trend in automatic speech recognition systems is the use of continuous mixture-density hidden Markov models (HMMs). Despite the good recognition performance that these systems achieve on average in large vocabulary applications, there is a large variability in performance across speakers. Performance degrades dramatically when the user is radically different from the training population. A popular technique that can improve the performance and robustness of a speech recognition system is adapting speech models to the speaker, and more generally to the channel and the task. In continuous mixture-density HMMs the number of component densities is typically very large, and it may not be feasible to acquire a sufficient amount of adaptation data for robust maximum-likelihood estimates. To solve this problem, the authors propose a constrained estimation technique for Gaussian mixture densities. The algorithm is evaluated on the large-vocabulary Wall Street Journal corpus for both native and nonnative speakers of American English. For nonnative speakers, the recognition error rate is approximately halved with only a small amount of adaptation data, and it approaches the speaker-independent accuracy achieved for native speakers. For native speakers, the recognition performance after adaptation improves to the accuracy of speaker-dependent systems that use six times as much training data.>

A statistical technique for vector quantizer (VQ) prototype adaptation, based on tied-mixture continuous-parameter HMM's, is derived and evaluated on the basis of experimental evidence. Performance on difficult adaptation tasks indicates that VQ-prototype adaptation via tied-mixture HMM's constitutes a useful mechanism for speaker adaptation, particularly when there are substantial channel differences or when there is a large mismatch between reference and target speaker characteristics.

SRI is developing a system that uses real time speech recognition to diag nose, evaluate and provide training in spoken English. The paper first describes the methods and results of a study of the feasibility of automati cally grading the performance of Japanese students when reading English aloud. Utterances recorded from Japanese speakers were independently rated by expert listeners. Speech grading software was developed from a speaker independent hidden-Markov-model speech recognition system. The auto matic grading procedure first aligned the speech with a model and then com pared the segments of the speech signal with models of those segments that have been developed from a database of speech from native speakers of English. The evaluation study showed that ratings of speech quality by experts are very reliable and automatic grades correlate well (r > 0.8) with those expert ratings. SRI is now extending this technology and integrating it in a spoken-language training system. This effort involves (1) porting SRI's DECIPHER speech recognition system to a microcomputer platform, and (2) extending the speech-evaluation software to more exactly diagnose a learner's pronuncia tion deficits and lead the learner through an appropriate regimen of exer cises.