10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

Automatic Intonation Classification for Speech Training Systems

György Szaszák, Dávid Sztahó, Klára Vicsi

BME, Hungary

A prosodic Hidden Markov model (HMM) based modality recognizer has been developed, which, after supra-segmental acoustic pre-processing, can perform clause and sentence boundary detection and modality (sentence type) recognition. This modality recognizer is adapted to carry out automatic evaluation of the intonation of the produced utterances in a speech training system for hearing-impaired persons or foreign language learners. The system is evaluated on utterances from normally-speaking persons and tested with speech-impaired (due to hearing problems) persons. To allow a deeper analysis, the automatic classification of the intonation is compared to subjective listening tests.

Full Paper

Bibliographic reference.  Szaszák, György / Sztahó, Dávid / Vicsi, Klára (2009): "Automatic intonation classification for speech training systems", In INTERSPEECH-2009, 1899-1902.