Nonlinear mapping for feature extraction in automatic speech...

Details Nonlinear mapping for feature extraction in automatic speech... Nonlinear mapping for feature extraction in automatic speech...

2007-08-07

2007-08-07

Armstrong, Angela (Department: 2626)

Data processing: speech signal processing, linguistics, language

Speech signal processing

Recognition

C704S232000, C704S240000

Reexamination Certificate

active

09714806

ABSTRACT:
The present invention successfully combines neural-net discriminative feature processing with Gaussian-mixture distribution modeling (GMM). By training one or more neural networks to generate subword probability posteriors, then using transformations of these estimates as the base features for a conventionally-trained Gaussian-mixture based system, substantial error rate reductions may be achieved. The present invention effectively has two acoustic models in tandem—first a neural net and then a GMM. By using a variety of combination schemes available for connectionist models, various systems based upon multiple features streams can be constructed with even greater error rate reductions.

REFERENCES:
patent: 5317673 (1994-05-01), Cohen et al.
patent: 5745649 (1998-04-01), Lubensky
N. Morgan and H. Bourlard, Continuous Speech Recognition, An introduction to hybrid HMM/connectionist approach, IEEE Signal Processing Magazine, 12(3): 25-42, May 1995.

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