Classification of Fricative Consonants for Speech Enhancement in Hearing Devices

Ying-Yee Kong, Ala Mullangi and Kostas Kokkinakis

PLOS ONE, 2014, vol. 9, issue 4, 1-8

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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0095001

DOI: 10.1371/journal.pone.0095001

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