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Technical Program

Paper Detail

Paper:	SLP-L12.5
Session:	Discriminative Training
Time:	Friday, May 19, 15:20 - 15:40
Presentation:	Lecture
Topic:	Speech and Spoken Language Processing: Feature Extraction and Modeling
Title:	Joint Discriminitive Front End and Back End Training for Improved Speech Recognition Accuracy
Authors:	Jasha Droppo, Alex Acero, Microsoft Research, United States
Abstract:	This paper presents a general discriminative training method for both the front end feature extractor and back end acoustic model of an automatic speech recognition system. The front end and back end parameters are jointly trained using the Rprop algorithm against a maximum mutual information (MMI) objective function. Results are presented on the Aurora 2 noisy English digit recognition task. It is shown that discriminative training of the front end or back end alone can improve accuracy, but joint training is considerably better.