Discriminative feature and model design for automatic speech recognition

AUTOMATIC SPEECH RECOGNITION Mazin Rahim, Yoshua Bengio and Yann LeCun AT&T Labs Research, 600 Mountain Avenue, Murray Hill, New Jersey 07974, USA ABSTRACT A system for discriminative feature and model design is presented for automatic speech recognition. Training based on minimum classi cation 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-speci c transformations. Experimental results on telephone-based connected digit recognition are presented.