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Classification of Fricative Consonants for Speech Enhancement in Hearing Devices
Kong, Ying-Yee Mullangi, Ala Kokkinakis, Kostas
Kong, Ying-Yee
Mullangi, Ala
Kokkinakis, Kostas
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Abstract
Objective
To investigate a set of acoustic features and classification methods for the classification of three groups of fricative consonants differing in place of articulation.
Method
A support vector machine (SVM) algorithm was used to classify the fricatives extracted from the TIMIT database in quiet and also in speech babble noise at various signal-to-noise ratios (SNRs). Spectral features including four spectral moments, peak, slope, Mel-frequency cepstral coefficients (MFCC), Gammatone filters outputs, and magnitudes of fast Fourier Transform (FFT) spectrum were used for the classification. The analysis frame was restricted to only 8 msec. In addition, commonly-used linear and nonlinear principal component analysis dimensionality reduction techniques that project a high-dimensional feature vector onto a lower dimensional space were examined.
Results
With 13 MFCC coefficients, 14 or 24 Gammatone filter outputs, classification performance was greater than or equal to 85% in quiet and at +10 dB SNR. Using 14 Gammatone filter outputs above 1 kHz, classification accuracy remained high (greater than 80%) for a wide range of SNRs from +20 to +5 dB SNR.
Conclusions
High levels of classification accuracy for fricative consonants in quiet and in noise could be achieved using only spectral features extracted from a short time window. Results of this work have a direct impact on the development of speech enhancement algorithms for hearing devices.
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Date
2014-04-18
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Public Library of Science
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Citation
Kong Y-Y, Mullangi A, Kokkinakis K (2014) Classification of Fricative Consonants for Speech Enhancement in Hearing Devices. PLoS ONE 9(4): e95001.
http://dx.doi.org/10.1371/journal.pone.0095001