Detecting laughter in spontaneous speech by constructing laughter bouts

Authors:
Yan-Xiong Li;Qian-Hua He
Affiliations:
School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China 510640;School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China 510640
Venue:
International Journal of Speech Technology
Year:
2011

Citing 9
Cited 0

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Abstract

Laughter frequently occurs in spontaneous speech (e.g. conversational speech, meeting speech). Detecting laughter is quite important for semantic analysis, highlight extraction, spontaneous speech recognition, etc. In this paper, we first analyze the characteristic differences between speech and laughter, and then propose an approach for detecting laughter in spontaneous speech. In the proposed approach, non-silence signal segments are first extracted from spontaneous speech by using voice activity detection, and then split into syllables. Afterward, the possible laughter bouts are constructed by merging adjacent syllables (using symmetrical Itakura distance measure and duration threshold) instead of using a sliding fixed-length window. Finally, hidden Markov models (HMMs) are used to recognize the possible laughter bouts as laughs, speech sounds or other sounds. Experimental evaluations show that the proposed approach can achieve satisfactory results in detecting two types of audible laughs (audible solo and group laughs). Precision rate, recall rate, and F1-measure (harmonic mean of precision and recall rate) are 83.4%, 86.1%, and 84.7%, respectively. Compared with the sliding-window-based approach, 4.9% absolute improvements in F1-measure are obtained. In addition, the laughter boundary errors obtained by the proposed approach are smaller than that obtained by the sliding-window-based approach.