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A noise-robust front-end for distributed speech recognition in mobile communications

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Abstract

This paper investigates a new front-end processing that aims at improving the performance of speech recognition in noisy mobile environments. This approach combines features based on conventional Mel-cepstral Coefficients (MFCCs), Line Spectral Frequencies (LSFs) and formant-like (FL) features to constitute robust multivariate feature vectors. The resulting front-end constitutes an alternative to the DSR-XAFE (XAFE: eXtended Audio Front-End) available in GSM mobile communications. Our results showed that for highly noisy speech, using the paradigm that combines these spectral cues leads to a significant improvement in recognition accuracy on the Aurora 2 task.

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Author information

Authors and Affiliations

  1. Speech and Signal Processing Lab., USTHB University of Science and Technology, Algiers, Algeria

    Djamel Addou, Malika Boudraa & Bachir Boudraa

  2. LARIHS Lab., Université de Moncton, Shippagan campus, New Brunswick, Canada

    Sid-Ahmed Selouani & Kaoukeb Kifaya

Authors
  1. Djamel Addou
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  2. Sid-Ahmed Selouani
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  3. Kaoukeb Kifaya
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  4. Malika Boudraa
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  5. Bachir Boudraa
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Corresponding author

Correspondence to Sid-Ahmed Selouani.

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Addou, D., Selouani, SA., Kifaya, K. et al. A noise-robust front-end for distributed speech recognition in mobile communications. Int J Speech Technol 10, 167–173 (2007). https://doi.org/10.1007/s10772-009-9025-9

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  • DOI: https://doi.org/10.1007/s10772-009-9025-9

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