Acoustic analysis is a noninvasive technique based on the digital processing of the speech signal. Acoustic analysis based techniques are an effective tool to support vocal and voice disease screening and especially in their early detection and diagnosis. Modern lifestyle has increased the risk of pathological voice problems. This work focuses on a robust, rapid and accurate system for automatic detection of normal and pathological speech and also to detect the type of pathology. This system employs non-invasive, inexpensive and fully automated measures of vocal tract characteristics and excitation information. Mel-frequency cepstral coefficients and linear prediction cepstral coefficients are used as acoustic features. The system uses Gaussian mixture model and hidden Markov model classifiers. Cerebral palsy, dysarthria, hearing impairments, laryngectomy, mental retardation, left side paralysis, quadriparesis, stammering, stroke, tumour in vocal tract are the types of pathologies considered in our experiments. From the experimental results, it is observed that to classify normal and pathological voice hidden Markov model with mel frequency cepstral coefficients with delta and acceleration coefficients is giving 94.44% efficiency. Likewise to identify the type of pathology Gaussian mixture model with mel frequency cepstral coefficients with delta and acceleration coefficients is giving 95.74% efficiency. 
Automatic system to detect the type of voice pathology