Name
Title | ||
|---|---|---|
State-based Gaussian selection in large vocabulary continuous speech recognition using HMMs |
Abstract | ||
|---|---|---|
This paper investigates the use of Gaussian selection (GS) to increase the speed of a large vocabulary speech recognition system. Typically, 30-70% of the computational time of a continuous density hidden Markov model-based (HMM-based) speech recognizer is spent calculating probabilities. The aim of CS is to reduce this load by selecting the subset of Gaussian component likelihoods that should be computed given a particular input vector. This paper examines new techniques for obtaining “good” Gaussian subsets or “shortlists.” All the new schemes make use of state information, specifically, to which state each of the Gaussian components belongs. In this way, a maximum number of Gaussian components per state may be specified, hence reducing the size of the shortlist. The first technique introduced is a simple extension of the standard GS method, which uses this state information. Then, more complex schemes based on maximizing the likelihood of the training data are proposed. These new approaches are compared with the standard GS scheme on a large vocabulary speech recognition task. On this task, the use of state information reduced the percentage of Gaussians computed to 10-15%, compared with 20-30% for the standard GS scheme, with little degradation in performance |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/89.748120 | IEEE Transactions on Speech and Audio Processing |
Keywords | Field | DocType |
Gaussian processes,hidden Markov models,speech coding,speech recognition,vector quantisation,Gaussian component likelihoods,Gaussian components,HMM,VQ,codewords,computational time,continuous density hidden Markov model,input vector,large vocabulary continuous speech recognition,probabilities,shortlists,state information,state-based Gaussian selection,training data | Speech processing,Speech coding,Pattern recognition,Computer science,Markov model,Speech recognition,Gaussian,Gaussian process,Artificial intelligence,Hidden Markov model,Vocabulary,Codebook | Journal |
Volume | Issue | ISSN |
7 | 2 | 1063-6676 |
Citations | PageRank | References |
27 | 3.37 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gales, M.J.F. | 1 | 82 | 8.13 |
| Kate Knill | 2 | 249 | 28.02 |
| S. J. Young | 3 | 1174 | 193.63 |