Abstract
In the past decade, much research has been done on automatic detection and classification of vocal fold disorders, and these tasks continue to require further investigation. The aim of this study is to develop systems that may help in diagnosing patients from their speech. The systems will perform voice disorder detection, classification of voice disorders, and digit recognition. To find the best system, we will compare the system performance when using different voice features. We are the first to explore the use relative spectral transform perceptual linear predictive (RASTA-PLP) feature for speech pathology. The speech samples used in most of the literature are sustained vowels, while the speech samples we worked on are words, which are more natural. To evaluate the performance of the developed system, we used a database containing five types of vocal fold disorders. The database includes a total of 142 speakers half of them were normal speakers. The best accuracy achieved for the voice disorder detection system was 92.40%. In the voice disorder classification system, the maximum obtained recognition rate by using words was 73%. For the digit recognition system, a recognition rate of 98.57% was obtained. PLP and RASTA_PLP showed better performance in the developed pathology assessment systems.