Abstract:
The classification of voice disorders has been hampered by the questionable reliability of perceptual scaling methods and by the generally poor correlations between perceptual and acoustic data. This report uses a self-organizing map to classify normal and disordered voices. The self-organizing feature map (SOFM) is essentially a two-dimensional representational mapping over an array of processing units of the multidimensional nonlinear regularities inherent in the input data space. The input data were five acoustic measures obtained from a MDVP [Multi-Dimensional Voice Protocol, Kay Elemetrics Corp.] analysis of sustained phonations included in the Voice Disorders Database version 1.03, compiled by the Voice and Speech Laboratory, Massachusetts Eye and Ear Infirmary. The measures included: jitter percent, fundamental frequency variation, shimmer percent, noise-to-harmonics ratio, and amplitude of the dominant cepstral rhamonic. The SOFM was trained with three voice samples: normal, hyperfunction, and anterior--posterior squeezing. The SOFM also was used to classify voices obtained from individuals with neurogenic speech disorders (dysarthrias). [Work supported by NIH.]