Automatic Laughter Detection in Spontaneous Speech Using GMM-SVM Method

Spontaneous conversations frequently contain various non-verbal vocalizations (such as laughter). The accuracy of a speech recognizer may decrease in the case of spontaneous speech because of these non-verbal vocalization phenomena. The aim of the present research is to develop an accurate and efficient method in order to recognize laughter in spontaneous utterances. We used GMM in modeling the data and SVM for differentiating laughter from other speech events. The training and testing of the laughter detector were carried out using the BEA Hungarian spoken language database. The results show that the GMM–SVM system seems to be a particularly good method for solving this problem.

[1]  J. Bachorowski,et al.  The acoustic features of human laughter. , 2001, The Journal of the Acoustical Society of America.

[2]  Nikki Mirghafori,et al.  Automatic laughter detection using neural networks , 2007, INTERSPEECH.

[3]  ALAN FOGEL,et al.  Laughter in mother-infant emotional communication , 1993 .

[4]  Ke Tang,et al.  Feature Selection for Maximizing the Area Under the ROC Curve , 2009, 2009 IEEE International Conference on Data Mining Workshops.

[5]  Hy Murveit,et al.  Spontaneous Speech Effects In Large Vocabulary Speech Recognition Applications , 1992, HLT.

[6]  Douglas E. Sturim,et al.  SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[7]  J. Goldstein.  The psychology of humor , 1972 .

[8]  S. Fiske,et al.  The Handbook of Social Psychology , 1935 .

[9]  Mária Gósy,et al.  BEA – A multifunctional Hungarian spoken language database , 2013 .

[10]  Daniel P. W. Ellis,et al.  Laughter Detection in Meetings , 2004 .

[11]  Paul Boersma,et al.  Praat, a system for doing phonetics by computer , 2002 .

[12]  Andrea Lockerd Thomaz,et al.  LAFCam: Leveraging affective feedback camcorder , 2002, CHI Extended Abstracts.

[13]  Sadaoki Furui.  Recent Progress in Corpus-Based Spontaneous Speech Recognition , 2005, IEICE Trans. Inf. Syst..

[14]  David A. van Leeuwen,et al.  Automatic discrimination between laughter and speech , 2007, Speech Commun..

[15]  Lie Lu,et al.  Highlight sound effects detection in audio stream , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[16]  Nick Campbell,et al.  No laughing matter , 2005, INTERSPEECH.

[17]  Alvin F. Martin,et al.  The DET curve in assessment of detection task performance , 1997, EUROSPEECH.

[18]  Norman N. Holland,et al.  Laughing, a psychology of humor , 1982 .

[19]  Sheri Hunnicutt,et al.  Acoustic analysis of laughter , 1992, ICSLP.

[20]  David A. van Leeuwen,et al.  Automatic detection of laughter , 2005, INTERSPEECH.