SRI’s DECIPHER System

SRI's DECIPHER System Hy Murveit, Michael Cohen, Patti Price, Gay Baldwin, Mitch Weintraub, and Jared Bernstein Speech Research Program, SRI International Menlo Park, CA 94025 Abstract SRI has developed a speaker-independent con- tinuous speech, large vocabulary speech recogni- tion system, DECIPHER, that provides state-of-the- art performance on the DARPA standard speaker- independent resource management training and test- ing materials. SRI's approach is to integrate speech and linguistic knowledge into the HMM framework. This paper describes performance improvements aris- ing from detailed phonological modeling and from the incorporation of cross-word coarticulatory constraints. 1 Introduction The hidden Markov model (HMM) formulation is a powerful statistical framework that is well-suited to the speech recognition problem. Systems based on this formulation have improved dramatically, however, as developers have learned how to modify them appropri- ately to take into account principles from speech re- search and from linguistics. Concepts arising from the study of the sound system of a language, i.e., phonol- ogy, are language-specific; they are done once for En- glish and little additional labor is required when, for example, the vocabulary or the domain changes. Care should be taken, however, in modeling detailed linguis- tic structure since the practice can lead to models with many additional parameters to be estimated; unless this problem is addressed directly, performance gains will be compromised. SRI is not the first group to incorporate speech knowledge and concepts from linguistics in an HMM formulation for speech recognition. In fact, many im- provements in I-IMM-based systems are implicitly re- lated to principles from speech and linguistics, even though this was not their original motivation. We sur- vey some of these modifications below. Phonetic Units. Not all recognizers are based on phonetic units. A number of HMM-based speech recognition systems have been based on word-level models [1, 12, 9]. The use of phonetic units allows for larger vocabularies by sharing training across sub- word units that repeat more frequently than words do. Phonetic-based units are now common to many HMM- based recognizers (e.g., [7, 8, 10]). Triphones. Triphones are phonetic models condi- tioned on the immediately surrounding phonetic units. BBN [3] was able to show significant performance gain (roughly halving the error rate compared to a similar system without the triphone models), pro- vided the context-dependent models were averaged ("smoothed") with the context-independent ones in or- der to deal with the large number of poorly trained triphone models. Triphones are used extensively now (e.g., at BBN, CMU, Lincoln Laboratories, SPd). Tri- phones can model major coarticulatory effects de- scribed in the speech research literature, as well as phonological variation conditioned on the immediately surrounding phones. In general, more detailed models will perform better than less detailed models, provided there is sufficient data to estimate the parameters. For 60 phones, there are 60 cubed triphones, which repre- sents a significant increase in parameters to estimate. Triphones would not have shown a performance gain had they been introduced without a mechanism to take this into account, i.e., in this case, smoothing with …

Content truncated to 500 words