Speech processing
Mohammad Bahador Najafi; Mansour Vali
Volume 14, Issue 2 , July 2020, , Pages 97-107
Abstract
After Alzheimer, Parkinson's disease is known as the most common malignant disease of the nervous system. One of the common obstacles of this disease is the expansion of speech disorders. Since the speech production in humans is made by combination of vibration of the vocal cords (phonatory section) ...
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After Alzheimer, Parkinson's disease is known as the most common malignant disease of the nervous system. One of the common obstacles of this disease is the expansion of speech disorders. Since the speech production in humans is made by combination of vibration of the vocal cords (phonatory section) and then passage through the resonator in vocal tract (articulatory section), it is expected that both of these sections to be impaired. In this study, by using a noninvasive method, it is intended to diagnose Parkinson's disease from speech signal of each subject; for this purpose, using 3 sustain vowels in Persian language recorded from 48 people (27 people with Parkinson's disease and 21 healthy people), it has been evaluated to assess the extent of damage to both phonatory and articulatory sections. The phonatory model can include features such as jitter, shimmer, fundamental frequencies, opening and closing cycling time of the glottal pulses. On the other hand, for the articulatory section, features such as first, second, and third formmants, zero crossing rates, MFFCs, and LPC are investigated. In this study, 38 feature categories were extracted and four statistical parameters of mean, standard deviation, skewness and kurtosis were calculated. Genetic Algorithm was used to identify the optimum features. Then, using the SVM, KNN and the Decision Tree classifiers, the optimum extracted features are classified to determine whether a person is patient or healthy. Finally for the main aim of this study, the results of both phonatory and articulatory sections were compared and challenged. The results of this study showed that phonatory features with accuracy of 96.1±1.2% were more useful than articulatory section in diagnosing of Parkinson. Also it was proved that vowel /u/ has more significant role in the diagnosis of Parkinson's disease compared to other vowels by accuracy of 97.6%.
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.
