Gammatone Wavelet Cepstral Coefficients for Robust Speech Recognition
Data(s) |
2013
|
---|---|
Resumo |
We develop noise robust features using Gammatone wavelets derived from the popular Gammatone functions. These wavelets incorporate the characteristics of human peripheral auditory systems, in particular the spatially-varying frequency response of the basilar membrane. We refer to the new features as Gammatone Wavelet Cepstral Coefficients (GWCC). The procedure involved in extracting GWCC from a speech signal is similar to that of the conventional Mel-Frequency Cepstral Coefficients (MFCC) technique, with the difference being in the type of filterbank used. We replace the conventional mel filterbank in MFCC with a Gammatone wavelet filterbank, which we construct using Gammatone wavelets. We also explore the effect of Gammatone filterbank based features (Gammatone Cepstral Coefficients (GCC)) for robust speech recognition. On AURORA 2 database, a comparison of GWCCs and GCCs with MFCCs shows that Gammatone based features yield a better recognition performance at low SNRs. |
Formato |
application/pdf |
Identificador |
http://eprints.iisc.ernet.in/49235/1/ieee_int_con_rec_2013.pdf Adiga, Aniruddha and Magimai-Doss, Mathew and Seelamantula, Chandra Sekhar (2013) Gammatone Wavelet Cepstral Coefficients for Robust Speech Recognition. In: IEEE International Conference of Region 10 (TENCON), OCT 22-25, 2013, Xian, PEOPLES R CHINA. |
Publicador |
IEEE |
Relação |
http://dx.doi.org/10.1109/TENCON.2013.6718948 http://eprints.iisc.ernet.in/49235/ |
Palavras-Chave | #Electrical Engineering |
Tipo |
Conference Proceedings NonPeerReviewed |