International Journal of Speech Technology

, Volume 21, Issue 4, pp 797–807 | Cite as

Mel scaled M-band wavelet filter bank for speech recognition

  • Prashant UpadhyayaEmail author
  • Omar Farooq
  • M. R. Abidi


A Mel scaled M-band wavelet filter bank structure is used to extract the robust acoustic feature for speech recognition application. The proposed filter bank can provide flexibility of frequency partition that decomposes the speech signal into the M-frequency band. To estimate the difference between Mel scaled M-band wavelet and dyadic wavelet filter bank, relative bandwidth deviation (RBD) and root mean square bandwidth deviation (RMSBD) with respect to baseline (Mel filter bank bandwidth) is calculated. Proposed filter bank gives 40.90 and 49.84% reduction for RBD and RMSBD respectively, over 24-dyadic wavelet filter bank. Feature extraction from the proposed filter bank using AMUAV corpus shows an improvement in terms of word recognition accuracy (WRA) at all SNR range (20 dB to 0 dB) over baseline (MFCC) features. For AMUAV corpus, the proposed feature shows the maximum improvement in WRA of 3.93% over baseline features and 3.90% over dyadic wavelet filter bank features. When applied to the VidTIMIT corpus, proposed features show the maximum improvement in WRA of 1.64% over baseline features and 4.43% over dyadic features.


M-band wavelet Dyadic MFCC Filter bank and feature extraction 



The authors would like to acknowledge Institution of Electronics and Telecommunication Engineers (IETE) for sponsoring the research fellowship during this period of research.


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics EngineeringAligarh Muslim UniversityAligarhIndia

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