Robust In-Car Speech Recognition Based on Nonlinear Multiple Regressions. (26th December 2006)

• Record Type: Journal Article
• Authors: Li Li, Weifeng Weifeng; Takeda Takeda, Kazuya Kazuya; Itakura Itakura, Fumitada Fumitada
• Other Names: Parthasarathy Parthasarathy S. S. Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2007(2007)
• Publication Date: 2006-12-26
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2007/16921
• Languages: English
• ISSNs: 1687-6172

Abstract

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.