System and method for creating generalized tied-mixture hidden Markov models for automatic speech recognition
AssigneeTexas Instruments Incorporated
InventorKaisheng Yao
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Abstract
A system for, and method of, creating generalized tied-mixture hidden Markov models (HMMs) for noisy automatic speech recognition. In one embodiment, the system includes: (1) an HMM estimator and state tyer configured to perform HMM parameter estimation and state-tying with respect to word transcriptions and a pronunciation dictionary to yield continuous-density HMMs and (2) a mixture tyer associated with the HMM estimator and state tyer and configured to tie Gaussian mixture components across states of the continuous-density HMMs and a phone confusion matrix thereby to yield the generalized tied-mixture HMMs.
Claims21
A system for creating generalized tied-mixture hidden Markov models (HMMS) for noisy automatic speech recognition, comprising:
- an HMM estimator and state tyer configured to perform HMM parameter estimation and state-tying with respect to word transcriptions and a pronunciation dictionary to yield continuous-density HMMs;
- and
- a mixture tyer associated with said HMM estimator and state tyer and configured to tie Gaussian mixture components across states of said continuous-density HMMs and a phone confusion matrix thereby to yield said generalized tied-mixture HMMs.
A method of creating generalized tied-mixture hidden Markov models (HMMS) for noisy automatic speech recognition, comprising:
- performing HMM parameter estimation and state-tying with respect to word transcriptions and a pronunciation dictionary to yield continuous-density HMMs;
- and
- tying Gaussian mixture components across states of said continuous-density HMMs and a phone confusion matrix thereby to yield said generalized tied-mixture HMMs.
A digital signal processor (DSP), comprising:
- data processing and storage circuitry controlled by a sequence of executable instructions configured to:
- perform hidden Markov models (HMM) parameter estimation and state-tying with respect to word transcriptions and a pronunciation dictionary to yield continuous-density HMMs;
- and
- tie Gaussian mixture components across states of said continuous-density HMMs and a phone confusion matrix thereby to yield said generalized tied-mixture HMMs.
Dependencies are inferred from the claim text.