Classification of Fricative Consonants for Speech Enhancement in Hearing Devices |
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Authors: | Ying-Yee Kong Ala Mullangi Kostas Kokkinakis |
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Institution: | 1. Department of Speech Language Pathology & Audiology, Northeastern University, Boston, Massachusetts, United States of America.; 2. Bioengineering Program, Northeastern University, Boston, Massachusetts, United States of America.; 3. Department of Speech-Language-Hearing, University of Kansas, Lawrence, Kansas, United States of America.; University of California, Irvine, United States of America, |
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Abstract: | ObjectiveTo investigate a set of acoustic features and classification methods for the classification of three groups of fricative consonants differing in place of articulation.MethodA 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.ResultsWith 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.ConclusionsHigh 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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