Discriminative feature and model design for automatic speech recognition
Mazin G. Rahim, Yoshua Bengio, Yann LeCun · 1997
A system for discriminative feature and model design is presented for automatic speech recognition. Training based on minimum classification error with a single objective function is applied for designing a set of parallel networks performing feature transformation and a set of hidden Markov models performing speech recognition. This paper compares the use of linear and non-linear functional transformations when applied to conventional recognition features, such as spectrum or cepstrum. It also provides a framework for integrated feature and model training when using class-specific transformations. Experimental results on telephone-based connected digit recognition are presented. 1. INTRODUCTION Improving the performance of hidden Markov model (HMM) based automatic speech recognition (ASR) systems has been a central issue that has dominated the entire field of speech recognition during the past two decades. One effort to improving HMMs has been by extending the training paradigm beyo...