We propose in this paper an automatic system to detect sigmatism from the speech signal. Sigmatism occurs when the tongue is positioned incorrectly during articulation of sibilant phones like /s/ and /z/. For our task we extracted various sets of features from speech: Mel frequency cepstral coefficients, energies in specific bandwidths of the spectral envelope, and the so-called supervectors, which are the parameters of an adapted speaker model. We then trained several classifiers on a speech database of German adults simulating three different types of sigmatism. Recognition results were calculated at a phone, word and speaker level for both the simulated database and for a database of pathological speakers. For the simulated database, we achieved recognition rates of up to 86%, 87% and 94% at a phone, word and speaker level. The best classifier was then integrated as part of a Java applet that allows patients to record their own speech, either by pronouncing isolated phones, a specific word or a list of words, and provides them with a feedback whether the sibilant phones are being correctly pronounced.

Index Terms: Gaussian Mixture Models, Support Vector Regression, Acoustic Analysis, Sigmatism