Recognition of vowels by cochlear implants using a fuzzy logic

Abstract

International audienceIn this work the authors have studied the application of a fuzzy logic for the modeling of human behavior. The subject is the recognition of phonemes by patients wearing a cochlear implant. The acoustic signal is coded by a cochlear implant and acquired on a working station and fed into the hard disk of a desktop computer. Parameters are then extracted from the signal to perform an automatic recognition of French vowels. Four vowels, embedded in short sentences, have been taken in order to construct two 48-item lists. First list was used for learning and second for recognition. Ten speakers collaborated in this experiment leading to about a thousand utterances. The lists were played to patients fitted with a cochlear implant. For each patient and for each speaker a confusion matrix was established. With parameters taken out of the electrical signal delivered by the implant, 255 recognition models were tested. Two automatic recognitions, using the leaming and the recognition lists, were clone with each model and the corresponding confusion matrices were calculated and compared with the matrix given by the patient. Recognitions were based on a fuzzy logic and on a Euclidian distance. Results showed that fuzzy logic led to results more close to the patients' decision than Euclidian distance. Then, useful parameters appear to depend strongly upon the patient and this must be considered for the setting of the electrodes