Abstract: Through studying the clinical characteristics of schizophrenic speech,the experiment collected 686 vowel data samples from 14 schizophrenic patients and 793 vowel data samples from 14 healthy controls matched with gender,age and education level to establish a pathological voice database.Using the improved formant extraction algorithm combining Ensemble Hilbert-Huang Transform (EHHT) and Cepstrum Interpolation (CI) to obtain the acoustic feature parameter set reflecting emotion change of schizophrenic voice quality,then combined with the Support Vector Machine (SVM) classifier for classification,automatic voice detection of schizophrenic patients and the healthy controls is achieved.Besides,it designed experiments to discuss the influence of the four factors,that is,the number and variance of white noise,the number of IMF components and the window length,on the detection effect,and compared with the classical formant estimation methods.Experimental results show that the detection accuracy of the proposed algorithm can reach 98.8%.The patients with schizophrenia have a significant difference in the acoustical parameters of the formants represent the sound quality feature with the healthy controls,and it may provide a new obJective,quantitative and efficient indicator for the clinical assistant diagnostic research of schizophrenia.

Key words: Cepstral interpolation, Ensemble hilbert-huang transform, Formant, Schizophrenic voice, Sound quality feature