In the paper recently proposed Human Factor Cepstral Coefficients (HFCC) are used to automatic recognition of pathological phoneme pronunciation in speech of impaired children and efficiency of this approach is compared to application of the standard Mel-Frequency Cepstral Coefficients (MFCC) as a feature vector. Both dynamic time warping (DTW), working on whole words or embedded phoneme patterns, and hidden Markov models (HMM) are used as classifiers in the presented research. Obtained results demonstrate superiority of combining HFCC features and modified phoneme-based DTW classifier.
Bibliographic reference. Wielgat, Robert / Zieliński, Tomasz P. / Świętojański, Paweł / Żołądź, Piotr / Król, Daniel / Woźniak, Tomasz / Grabias, Stanisław (2007): "Comparison of HMM and DTW methods in automatic recognition of pathological phoneme pronunciation", In INTERSPEECH-2007, 1705-1708.