Modeling sub-band correlation for noise-robust speech recognition

James McAuley*, Ji Ming, Philip Hanna, Darryl Stewart

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)


This paper investigates the effect of modeling sub-band correlation for noisy speech recognition. Sub-band data streams are assumed to be independent in many sub-band based speech recognition systems. However, the structure and operation of the human vocal tract suggests this assumption is unrealistic. A novel method is proposed to incorporate correlation into sub-band speech feature streams. In this method, all possible combinations of sub-bands are created and each combination is treated as a single frequency band by calculating a single feature vector for it. The resulting feature vectors capture information about every band in the combination as well as the dependency across the bands. Experiments conducted on the TIDigits database demonstrate significantly improved robustness in comparison to an independent sub-band system in the presence of both stationary and non-stationary noise.

Original languageEnglish
Publication statusPublished - 28 Sep 2004
EventProceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada
Duration: 17 May 200421 May 2004


ConferenceProceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing
CityMontreal, Que

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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