title: Classification of fricative consonants for speech enhancement in hearing devices.
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.
  
  
  METHODS
  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.
authors.affiliation: Department of Speech Language Pathology & Audiology, Northeastern University, Boston, Massachusetts, United States of America; Bioengineering Program, Northeastern University, Boston, Massachusetts, United States of America.
authors.forename: Ying-Yee
authors.initials: YY
authors.lastname: Kong
authors.affiliation: Bioengineering Program, Northeastern University, Boston, Massachusetts, United States of America.
authors.forename: Ala
authors.initials: A
authors.lastname: Mullangi
authors.affiliation: Department of Speech-Language-Hearing, University of Kansas, Lawrence, Kansas, United States of America.
authors.forename: Kostas
authors.initials: K
authors.lastname: Kokkinakis
mesh_terms: D006310:Hearing Aids; D006801:Humans; D013060:Speech; D060388:Support Vector Machine
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