Abstract
The [s] samples of 11 women, psychoacoustically classified as acceptable/unacceptable, were studied with the self-organizing map, the neural network algorithm of Kohonen. The measurement map had been previously computed with nondisordered speech samples. Fifteen-component spectral vectors, analyzed with the map, were calculated from short-time FFT spectra at 10-ms intervals. The degree of audible acceptability correlated with the location of the sample on the map. Spectral model vectors in different map locations depicted distinguishing spectral features in the [s] samples analyzed. The results demonstrate that self-organized maps are suitable for the extraction and measurement of acoustic features underlying psychoacoustic classifications, and for on-line visual imaging of speech.
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