Speech processing
Ehsan Akafi; Mansour Vali; Negin Moradi
Volume 6, Issue 3 , June 2012, , Pages 119-129
Abstract
Hypernasality is a frequently occurring resonance disorder in children with cleft palate. Generally an operation is necessary to reduce the hypernasality and therefore an assessment of hypernasality is imperative to quantify the effect of the surgery and design the speech therapy sessions which are crucial ...
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Hypernasality is a frequently occurring resonance disorder in children with cleft palate. Generally an operation is necessary to reduce the hypernasality and therefore an assessment of hypernasality is imperative to quantify the effect of the surgery and design the speech therapy sessions which are crucial after surgery. In this study, a new quantitative method is proposed to estimate hypernasality. The proposed method used the fact that an Autoregressive (AR) model for vocal tract system of a patient with hypernasal speech is not accurate; because of the zeros appear in the frequency response of vocal tract system due to existence of extra channel between oral and nasal cavity of these patients. Therefore in our method hypernasality was estimated by a quantity calculated from comparing the distance between the sequences of cepstrum coefficients extracted from AR model and Autoregressive Moving Average (ARMA) model. K-means and Bayes theorem were utilized for finding a threshold value for proposed index to classify the utterances of subjects. We achieved the balanced accuracy up to 82.18% on utterances and 97.72% on subjects. Since the proposed method needs only computer processing of speech data, compare to other clinical methods it is provides a simple evaluation of hypernasality.
