Diagnosis of vocal and voice disorders by the speech signal

We present a neural network application to the diagnosis of vocal and voice disorders, these disorders should be diagnosed in the early stage and normally cause changes in the voice signal. So we use acoustic parameters extracted from the voice as inputs for the neural network. In this paper, we focus our application on the distinction between pathologic and nonpathologic voices. The performance of the neural network is very good, 100% percent correct in the test. Furthermore, we have used neural network techniques to reduce the initial number of inputs (35), we conclude that only two acoustic parameters are needed for the classification between normal and pathological voices. The application can be a very useful diagnostic tool because it is noninvasive, makes possible to develop an automatic computer-based diagnosis system, is objective and can also be useful for evaluation of surgical, pharmacological and rehabilitation processes. Finally, we discuss the limitation of our work and possible future research.