Robust phoneme recognition for a speech therapy environment

Traditional speech therapy approaches for speech sound disorders have a lot of advantages to gain from computer-based therapy systems. In this paper, we propose a robust phoneme recognition solution for an interactive environment for speech therapy. With speech recognition techniques the motivation elements of computer-based therapy systems can be automated in order to get an interactive environment that motivates the therapy attendee towards better performances. The contribution of this paper is a robust phoneme recognition to control the feedback provided to the patient during a speech therapy session. We compare the results of hierarchical and flat classification, with naive Bayes, support vector machines and kernel density estimation on linear predictive coding coefficients and Mel-frequency cepstral coefficients.