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An FFT-Based Companding Front End for Noise-Robust Automatic Speech Recognition

EURASIP Journal on Audio, Speech, and Music Processing volume 2007, Article number: 065420 (2007) Cite this article

Abstract

We describe an FFT-based companding algorithm for preprocessing speech before recognition. The algorithm mimics tone-to-tone suppression and masking in the auditory system to improve automatic speech recognition performance in noise. Moreover, it is also very computationally efficient and suited to digital implementations due to its use of the FFT. In an automotive digits recognition task with the CU-Move database recorded in real environmental noise, the algorithm improves the relative word error by 12.5% at -5 dB signal-to-noise ratio (SNR) and by 6.2% across all SNRs (-5 dB SNR to +5 dB SNR). In the Aurora-2 database recorded with artificially added noise in several environments, the algorithm improves the relative word error rate in almost all situations.

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Authors and Affiliations

  1. Mitsubishi Electric Research Laboratories (MERL), 201 Broadway, Cambridge, MA, 02139-4307, USA

    Bhiksha Raj & Bent Schmidt-Nielsen

  2. Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139, USA

    Lorenzo Turicchia & Rahul Sarpeshkar

Authors
  1. Bhiksha Raj
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  2. Lorenzo Turicchia
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  3. Bent Schmidt-Nielsen
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  4. Rahul Sarpeshkar
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Corresponding author

Correspondence to Bhiksha Raj.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://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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Raj, B., Turicchia, L., Schmidt-Nielsen, B. et al. An FFT-Based Companding Front End for Noise-Robust Automatic Speech Recognition. J AUDIO SPEECH MUSIC PROC. 2007, 065420 (2007). https://doi.org/10.1155/2007/65420

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  • DOI: https://doi.org/10.1155/2007/65420

Keywords

  • Error Rate
  • Acoustics
  • Recognition Task
  • Recognition Performance
  • Auditory System