Cepstral Amplitude Range Normalization for Noise Robust Speech Recognition

• YOSHIZAWA Shingo

  Graduate School of Engineering, Hokkaido University

• HAYASAKA Noboru

  Graduate School of Engineering, Hokkaido University

• WADA Naoya

  Graduate School of Engineering, Hokkaido University

• MIYANAGA Yoshikazu

  Graduate School of Engineering, Hokkaido University

Abstract

This paper describes a noise robustness technique that normalizes the cepstral amplitude range in order to remove the influence of additive noise. Additive noise causes speech feature mismatches between testing and training environments and it degrades recognition accuracy in noisy environments. We presume an approximate model that expresses the influence by changing the amplitude range and the DC component in the log-spectra. According to this model, we propose a cepstral amplitude range normalization (CARN) that normalizes the cepstral distance between maximum and minimum values. It can estimate noise robust features without prior knowledge or adaptation. We evaluated its performance in an isolated word recognition task by using the Noisex92 database. Compared with the combinations of conventional methods, the CARN could improve recognition accuracy under various SNR conditions.

Journal

• IEICE Trans. on Information and Systems

  IEICE Trans. on Information and Systems 87 (8), 2130-2137, 2004-08-01

  The Institute of Electronics, Information and Communication Engineers

Keywords

• speech recognition
• robust features
• cepstrum
• Noisex92

Details

• CRID: 1574231877209345792
• NII Article ID: 110003214105
• NII Book ID: AA10826272
• ISSN: 09168532
• Text Lang: en
• Data Source:
  • CiNii Articles