title: Acoustic pattern recognition of /s/ misarticulation by the self-organizing map.
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.
authors.affiliation: Department of Phonetics, University of Helsinki, Finland.
authors.forename: R
authors.initials: R
authors.lastname: Mujunen
authors.forename: L
authors.initials: L
authors.lastname: Leinonen
authors.forename: J
authors.initials: J
authors.lastname: Kangas
authors.forename: K
authors.initials: K
authors.lastname: Torkkola
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001184:Articulation Disorders / Q000175:diagnosis*; D003196:Computer Graphics; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D016571:Neural Networks, Computer; D010700:Phonetics; D011571:Psychoacoustics; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D013018:Sound Spectrography / Q000295:instrumentation*