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Link to original content: https://doi.org/10.1155/2007/16921
Robust In-Car Speech Recognition Based on Nonlinear Multiple Regressions | EURASIP Journal on Advances in Signal Processing | Full Text
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Robust In-Car Speech Recognition Based on Nonlinear Multiple Regressions

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 016921 (2006) Cite this article

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.

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Authors and Affiliations

  1. Graduate School of Information Science, Nagoya University, Nagoya, 464-8603, Japan

    Weifeng Li & Kazuya Takeda

  2. Department of Information Engineering, Faculty of Science and Technology, Meijo University, Nagoya, 468-8502, Japan

    Fumitada Itakura

Authors
  1. Weifeng Li
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  2. Kazuya Takeda
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  3. Fumitada Itakura
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Correspondence to Weifeng Li.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Li, W., Takeda, K. & Itakura, F. Robust In-Car Speech Recognition Based on Nonlinear Multiple Regressions. EURASIP J. Adv. Signal Process. 2007, 016921 (2006). https://doi.org/10.1155/2007/16921

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