The self-organizing map (a neural network) was applied to the spectral pattern recognition of voice quality in 34 subjects: 15 patients operated on because of insufficient glottal closure and 19 subjects not treated for voice disorders. The voice samples, segments of sustained /a/, were perceptually rated by six experts. A self-organized acoustic feature map was first computed from tokens of /a/ and then used for the analysis of the samples. The locations of the samples on the map were determined and the distances from a normal reference were compared with the perceptual ratings. The map locations corresponded to the degree of audible disorder: the samples judged as normal were overlapping or close to the normal reference, whereas the samples judged as dysphonic were located further away from it. The comparison of pre- and postoperative samples of the patients showed that the perceived improvement of voice quality was also detected by the map.
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