Automatic detection of sigmatism in children

Cassia Valentini-Botinhao, Sabine Degenkolb-Weyers, Andreas Maier, Ulrich Eysholdt, Tobias Bocklet, Elmar Nöth

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.
Original languageEnglish
Title of host publicationProc. WOCCI
Place of PublicationPortland, USA
Publication statusPublished - 1 Sep 2012

Fingerprint

Dive into the research topics of 'Automatic detection of sigmatism in children'. Together they form a unique fingerprint.

Cite this