Your browser version may not work well with NCBI's Web applications. More information here...
1: Conf Proc IEEE Eng Med Biol Soc. 2006;1:2199-202.Click here to read Links

Speech sound classification and detection of articulation disorders with support vector machines and wavelets.

Georgoulas G, Georgopoulos VC, Stylios CD.

Lab. for Autom. & Robotics, Patras Univ, 26500 Patras, Greece. georgoul@ee.upatras.gr

This paper proposes a novel integrated methodology to extract features and classify speech sounds with intent to detect the possible existence of a speech articulation disorder in a speaker. Articulation, in effect, is the specific and characteristic way that an individual produces the speech sounds. A methodology to process the speech signal, extract features and finally classify the signal and detect articulation problems in a speaker is presented. The use of support vector machines (SVMs), for the classification of speech sounds and detection of articulation disorders is introduced. The proposed method is implemented on a data set where different sets of features and different schemes of SVMs are tested leading to satisfactory performance.

PMID: 17946095 [PubMed - indexed for MEDLINE]