Title :
Acoustic adaptation using nonlinear transformations of HMM parameters
Author :
Abrash, Victor ; Sankar, Ananth ; Franco, Horacio ; Cohen, Michael
Author_Institution :
Speech Res. & Technol. Lab., SRI Int., Menlo Park, CA, USA
Abstract :
Speech recognition performance degrades significantly when there is a mismatch between testing and training conditions. Linear transformation-based maximum-likelihood (ML) techniques have been proposed recently to tackle this problem. We extend this approach to use nonlinear transformations. These are implemented by multilayer perceptrons (MLPs) which transform the Gaussian means. We derive a generalized expectation-maximization (GEM) training algorithm to estimate the MLP weights. Some preliminary experimental results on nonnative speaker adaptation are presented
Keywords :
acoustic signal processing; adaptive signal processing; learning (artificial intelligence); maximum likelihood estimation; multilayer perceptrons; speech processing; speech recognition; Gaussian means; HMM parameters; MLP; MLP weights estimation; acoustic adaptation; experimental results; generalized expectation-maximization training algorithm; maximum-likelihood techniques; multilayer perceptrons; nonlinear transformations; nonnative speaker adaptation; speech recognition performance; testing conditions; training conditions; Acoustic testing; Automatic speech recognition; Degradation; Hidden Markov models; Microphones; Multilayer perceptrons; Noise robustness; Nonlinear acoustics; Speech enhancement; Speech recognition;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on
Print_ISBN :
0-7803-3192-3
DOI :
10.1109/ICASSP.1996.543224
