Speech sound classification and detection of articulation disorders with support vector machines and wavelets.
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.
Georgoulas
George
G
Lab. for Autom. & Robotics, Patras Univ, 26500 Patras, Greece. georgoul@ee.upatras.gr
Georgopoulos
Voula C
VC
Stylios
Chrysostomos D
CD
D000465:Algorithms; D001184:Articulation Disorders / Q000175:diagnosis* / Q000503:physiopathology; D001185:Artificial Intelligence; D002648:Child; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013018:Sound Spectrography / Q000379:methods*; D013068:Speech Production Measurement / Q000379:methods*; D049250:Speech Recognition Software