Technical Program

Paper Detail

Paper:	SLP-P11.5
Session:	Front-end For Robust Speech Recognition
Time:	Wednesday, May 17, 16:30 - 18:30
Presentation:	Poster
Topic:	Speech and Spoken Language Processing: Feature-based Robust Speech Recognition (e.g., noise, etc)
Title:	NOISE ROBUST AURORA-2 SPEECH RECOGNITION EMPLOYING A CODEBOOK-CONSTRAINED KALMAN FILTER PREPROCESSOR
Authors:	Krishnan Venkatesh, Sabato M. Siniscalchi, David V. Anderson, Mark A. Clements, Georgia Institute of Technology, United States
Abstract:	In this paper, a speech signal estimation framework involving Kalman filters for use as a front-end to the Aurora-2 speech recognition task is presented. Kalman filter based speech estimation algorithms assume autoregressive (AR) models for the speech and the noise signals. In this paper, the parameters of the AR models are estimated using a expectation–maximization approach. The key to the success of the proposed algorithm is the constraint on the AR model parameters corresponding to the speech signal to belong to a codebook trained on AR parameters obtained from clean speech signals. Aurora-2 noise-robust speech recognition experiments are performed to demonstrate the success of the codebook-constrained Kalman filter in improving speech recognition accuracy in noisy environments.Results with both clean and multi-conditional training are provided to illustrate the improvements in the recognition accuracy compared to the base-line system where no pre-processing is employed.