Title :
Screening for high risk suicidal states using mel-cepstral coefficients and energy in frequency bands
Author :
Keskinpala, Hande Kaymaz ; Yingthawornsuk, Thaweesak ; Wilkes, D. Mitch ; Shiavi, Richard G. ; Salomon, Ronald M.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Vanderbilt Univ., Nashville, TN, USA
Abstract :
Distinguishing high risk suicidal patients from less severely depressed patients at low risk is a critical problem. This paper describes a novel way to address this issue. The vocal characteristics of male and female speech samples from high risk suicidal and depressed patients were analyzed and distinguished using mel-cepstral coefficients and using energy in frequency bands. Two kinds of speech samples, one from an interview session and the other from a reading session, were analyzed. The results show that mel-cepstral coefficients and energy in frequency bands may be used to separate these populations and the controlled reading tended to provide better results than the interview.
Keywords :
adaptive signal processing; blind source separation; cepstral analysis; medical signal processing; speech; speech processing; speech recognition; depressed patient vocal characteristic analysis; female speech sample vocal characteristics; frequency band energy; high risk suicidal patient; high risk suicidal state; interview session-derived speech sample; less severely depressed patient; low risk suicidal patient; mel-cepstral coefficient; reading session-derived speech sample; speech sample analysis; suicidal patient identification; suicidal patient vocal characteristics analysis; suicidal state screening; Cepstral analysis; Databases; Feature extraction; Interviews; Speech; Speech processing;
Conference_Titel :
Signal Processing Conference, 2007 15th European
Conference_Location :
Poznan
Print_ISBN :
978-839-2134-04-6