Combining temporal and cepstral features for the automatic perceptual categorization of disordered connected speech
نویسندگان
چکیده
The objective of the presentation is to report experiments involving the automatic classification of disordered connected speech into multiple (modal, moderately hoarse, severely hoarse) categories. Support vector machines, used for the classification, have been fed with temporal signal-to-dysperiodicity ratios, the first rahmonic amplitude as well as mel-frequency cepstral coefficients. The signal-to-dysperiodicity ratio complements the first rahmonic amplitude when categorizing voice samples according to the degree of hoarseness yielding 77% of correct classification.
منابع مشابه
Automatic perceptual categorization of disordered connected speech
The objective of the presentation is to report experiments involving the automatic classification of disordered connected speech into binary (normal, pathological) or multiple (modal, moderately hoarse, severely hoarse) categories. The multicategory classification according to the perceived degree of hoarseness is considered to be clinically meaningful and desirable given that the reliable perc...
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