Robust in-car speech recognition based on nonlinear multiple regressions

Authors:
Weifeng Li;Kazuya Takeda;Fumitada Itakura
Affiliations:
Graduate School of Information Science, Nagoya University, Nagoya, Japan;Graduate School of Information Science, Nagoya University, Nagoya, Japan;Department of Information Engineering, Faculty of Science and Technology, Meijo University, Nagoya, Japan
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2007

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Abstract

We address issues for improving handsfree speech recognition performance in different car environments using a single distant microphone. In this paper, we propose a nonlinear multiple-regression-based enhancement method for in-car speech recognition. In order to develop a data-driven in-car recognition system, we develop an effective algorithm for adapting the regression parameters to different driving conditions. We also devise the model compensation scheme by synthesizing the training data using the optimal regression parameters and by selecting the optimal HMM for the test speech. Based on isolated word recognition experiments conducted in 15 real car environments, the proposed adaptive regression approach shows an advantage in average relative word error rate (WER) reductions of 52.5% and 14.8%, compared to original noisy speech and ETSI advanced front end, respectively.