Novel Cochlear Filter Based Cepstral Coefficients for Classification of Unvoiced Fricatives
Namrata Singh, Nikhil Bhendawade, Hemant A. Patil · International Journal on Natural Language Computing · 2014
In this paper, the use of new auditory-based features derived from cochlear filters, have been proposed for classification of unvoiced fricatives.Classification attempts have been made to classify sibilant (i.e., /s/, /sh/) vs. non-sibilants (i.e., /f/, /th/) as well as for fricatives within each sub-category (i.e., intra-sibilants and intra-non-sibilants).Our experimental results indicate that proposed feature set, viz., Cochlear Filterbased Cepstral Coefficients (CFCC) performs better for individual fricative classification (i.e., a jump of 3.41 % in average classification accuracy and a fall of 6.59 % in EER) in clean conditions than the stateof-the-art feature set, viz., Mel Frequency Cepstral Coefficients (MFCC).Furthermore, under signal degradation conditions (i.e., by additive white noise) classification accuracy using proposed feature set drops much slowly (i.e., from 86.73 % in clean conditions to 77.46 % at SNR of 5 dB) than by using MFCC (i.e., from 82.18 % in clean conditions to 46.93 % at SNR of 5 dB).