[Use of self-organizing neural networks (Kohonen maps) for classification of voice acoustic signals exemplified by the infant voice with and without time-delayed auditory feedback].

Subjective and auditory assessment of the voice is now more commonly being replaced by objective voice analysis. Because of the amount of data available from computer-aided voice analysis, subjective selection and interpretation of single data sets remain a matter of experience of the individual investigator. Since neuronal networks are widely used in telecommunication and speech recognition, we applied self-organizing Kohonen networks to classify voice patterns. In the phase of "learning," the Kohonen map is adapted to patterns of the primary signals obtained. If, in the phase of using the map, the input signal hits the field of the primary signals, it will resemble them closely. In this study, we recorded newborn and young infant cries using a DAT recorder and a high-quality microphone. The cries were elicited by wearing uncomfortable headphones ("cries of discomfort"). Spectrographic characteristics of the cries were classified by 20-step bark spectra and then applied to the neuronal networks. It was possible to recognize similarities of different cries of the same children and interindividual differences, as well as cries of children with profound hearing loss. In addition, delayed auditory feedback at 80 dB SL was presented to 27 children via headphone using a three-headed tape-recorder as a model for induced individual cry changes. However, it was not possible to classify short-term changes as in a delayed feedback procedure. Nevertheless, neuronal networks may be helpful as an additional tool in spectrographic voice analysis.