Neural Computing and Applications

, Volume 22, Issue 7–8, pp 1321–1327 | Cite as

Robust speech recognition based on independent vector analysis using harmonic frequency dependency

  • Soram Jun
  • Minook Kim
  • Myungwoo Oh
  • Hyung-Min Park
ICONIP 2011

Abstract

This paper describes an algorithm that enhances speech by independent vector analysis (IVA) using harmonic frequency dependency for robust speech recognition. While the conventional IVA exploits the full-band uniform dependencies of each source signal, a harmonic clique model is introduced to improve the enhancement performance by modeling strong dependencies among multiples of fundamental frequencies. An IVA-based learning algorithm is derived to consider the non-holonomic constraint and the minimal distortion principle to reduce the unavoidable distortion of IVA, and the minimum power distortionless response beamformer is used as a pre-processing step. In addition, the algorithm compares the log-spectral features of the enhanced speech and observed noisy speech to identify time–frequency segments corrupted by noise and restores those with the cluster-based missing feature reconstruction technique. Experimental results demonstrate that the proposed method enhances recognition performance significantly in noisy environments, especially with competing interference.

Keywords

Robust speech recognition Independent vector analysis Missing feature technique Blind source separation 

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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Soram Jun
    • 1
  • Minook Kim
    • 1
  • Myungwoo Oh
    • 1
  • Hyung-Min Park
    • 1
  1. 1.Department of Electronic EngineeringSogang UniversityMapo-gu, SeoulRepublic of Korea

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