Third International Conference on Spoken Language Processing (ICSLP 94)

Yokohama, Japan
September 18-22, 1994

Nonlinear Time Alignment in Stochastic Trajectory Models for Speech Recognition

Mohamed Afify (1,2), Yifan Gong (1), Jean-Paul Haton (1)

(1) CRIN-CNRS & INRIA Lorraine, BP 239, Nancy, France
(2) National Telecommunication Institute, Cairo, Egypt

A nonlinear time alignment technique is presented in the framework of stochastic trajectory models (STM). We show how to obtain maximum likelihood (ML) estimates of model parameters, and how to use the technique during recognition with a slight additional computational overhead. Experimental results for a French 850 word continuous speech task are given. For a 10 speaker population, we test with various degrees of nonlinearity, and the introduced technique provides a slight improvement (about 1%) in the average word recognition rate.

Full Paper

Bibliographic reference.  Afify, Mohamed / Gong, Yifan / Haton, Jean-Paul (1994): "Nonlinear time alignment in stochastic trajectory models for speech recognition", In ICSLP-1994, 291-294.