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Robust In-Car Speech Recognition Based on Nonlinear Multiple Regressions

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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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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Keywords

  • Word Recognition
  • Speech Recognition
  • Enhancement Method
  • Word Error Rate
  • Noisy Speech