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Many individuals are subjected to the risk of voice disorders which may be characterized by hoarseness, vocal fatigue,periodic loss of voice or inappropriate pitch or loudness. These disordered voice cause changes in the acoustic characteristics. Therefore, the voice signal is used as an important measure to diagnose them. This paper deals the classification of normal and two disordered voices using...
The paper proposes a new method for the phoneme independent normal and pathological voice classification. The new method proposes a wavelet sub band based hybrid classifier built by combining Gaussian Mixture Model-Universal Background Model (GMM-UBM) and Support Vector Machine (SVM). The Mel Frequency Cepstral Coefficients (MFCCs) are computed for each sub band obtained by wavelet decomposition....
This paper proposes a new approach for the phoneme independent pathological voice detection. The phonemes /a/, /i/, /u/ from normal and subjects suffering from voice disorders are recorded. The system uses wavelet based Mel Frequency Cepstral Coefficients (MFCCs) as features, which are given to Gaussian Mixture Model-Universal Background Model (GMM-UBM) classifier. The MFCCs are computed for each...
This paper proposes a text independent method for the classification of normal and pathological voices. If the classifier is text dependent i.e classifier is trained for a particular phoneme, then it may difficult for the patient to pronounce the particular phoneme. To overcome this difficulty, a text independent classification method is proposed, which uses Mel-Frequency Cepstral Coefficients (MFCCs)...
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