Skip to main content
SpringerLink
Log in

Detecting laughter in spontaneous speech by constructing laughter bouts

  • Published:
International Journal of Speech Technology Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Bachorowski, J. A., Smoski, M. J., & Owren, M. J. (2001). The acoustic features of human laughter. The Journal of the Acoustical Society of America, 110(3), 1581–1597.

    Article  Google Scholar 

  • Bickley, C., & Hunnicutt, S. (1992). Acoustic analysis of laughter. Paper presented at the Int. Conf. on Spoken Language Processing, Banff, Canada, 12–16 Oct.

  • Burileanu, D., Pascalin, L., Burileanu, C., & Puchiu, M. (2000). An adaptive and fast speech detection algorithm. Paper presented at the The Third Int. Workshop on Text, Speech and Dialogue, Brno, Czech Republic, 13–16 Sept.

  • Cai, R., Lie, L., Zhang, H. J., & Cai, L. H. (2003). Highlight sound effects detection in audio stream. Paper presented at the IEEE Int. Conf. on Multimedia and Expo, Baltimore, MD, USA, 6–9 July.

  • Carter, A. (2000). Automatic acoustic laughter detection. Unpublished Master thesis, Keele University, Staffordshire, UK.

  • Dubnowski, J., Schafer, R., & Rabiner, L. (1976). Real-time digital hardware pitch detector. IEEE Transactions on Acoustics, Speech, and Signal Processing, 24(1), 2–8.

    Article  Google Scholar 

  • Gray, J. A. H., & Markel, J. D. (1976). Distance measures for speech processing. IEEE Transactions on Acoustics, Speech, and Signal Processing, 24(5), 380–391.

    Article  MathSciNet  Google Scholar 

  • Itakura, F. (1975). Minimum prediction residual principle applied to speech recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 23(1), 67–72.

    Article  Google Scholar 

  • Kennedy, L. S., & Ellis, D. P. W. (2004). Laughter detection in meetings. Paper presented at the NIST International Conference on Acoustics, Speech and Signal Processing (Meeting Recognition Workshop), Montreal, Canada, 17 May.

  • Knox, M. T., & Mirghafori, N. (2007). Automatic laughter detection using neural networks. Paper presented at the Interspeech, Antwerpen, Belgium, 27–31 Aug.

  • Knox, M. T., Morgan, N., & Mirghafori, N. (2008). Getting the last laugh: automatic laughter segmentation in meetings. Paper presented at the Interspeech, Brisbane, Australia, 22–26 Sept.

  • Laskowski, K. (2008). Modeling vocal interaction for text-independent detection of involvement hotspots in multi-party meetings. Paper presented at the 2nd IEEE/ISCA/ACL Workshop on Spoken Language Technology, Goa, India, 15–19 Dec.

  • Li, A.-J., Yin, Z.-G., Wang, M.-L., Xu, B., & Zong, C. Q. (2002). Spontaneous conversation corpus CADCC. Beijing: Phonetics Lab., Institute of Linguistics Chinese Academy of Social Sciences.

    Google Scholar 

  • Li, Y. X., He, Q. H., Chen, N., & Qi, Z. H. (2008). Spectral stability feature based novel method for discriminating speech and laughter. Journal of Electronics & Information Technology, 30(6), 1359–1362.

    Article  Google Scholar 

  • Li, Y. X., He, Q. H., Kwong, S., Li, T., & Yang, J. C. (2009). Characteristics-based effective applause detection for meeting speech. Signal Processing, 89(8), 1625–1633.

    Article  MATH  Google Scholar 

  • Makhoul, J., Viswanathan, R., Cosell, L., & Russell, W. (1974). Natural communication with computers: speech compression research at BBN (No. 2976). Cambridge: Bolt Beranek and Newman.

    Google Scholar 

  • O’Shaughnessy, D. (2008). Automatic speech recognition: history, methods and challenges. Pattern Recognition, 41(10), 2965–2979.

    Article  MATH  Google Scholar 

  • Petridis, S., & Pantic, M. (2008). Audiovisual discrimination between laughter and speech. Paper presented at the IEEE ICASSP 2008, Las Vegas, Nevada, USA, 30 Mar.–4 Apr.

  • Provine, R. R. (1993). Laughter punctuates speech: linguistic, social and gender contexts of laughter. Ethology, 92, 291–298.

    Google Scholar 

  • Provine, R. R. (1996). Laughter. American Scientist, 84(1), 38–47.

    Google Scholar 

  • Rabiner, L. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77, 257–286.

    Article  Google Scholar 

  • Rabiner, L., & Juang, B.-H. (1993). Fundamentals of speech recognition. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • Schuller, B., Eyben, F., & Rigoll, G. (2008). Static and dynamic modeling for the recognition of non-verbal vocalisations in conversational speech. In Perception in multimodal dialogue systems (Vol. 5078, pp. 99–110). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Temko, A., Macho, D., & Nadeu, C. (2008). Fuzzy integral based information fusion for classification of highly confusable non-speech sounds. Pattern Recognition, 41(5), 1814–1823.

    Article  MATH  Google Scholar 

  • Truong, K. P., & van Leeuwen, D. A. (2007). Automatic discrimination between laughter and speech. Speech Communication, 49(2), 144–158.

    Article  Google Scholar 

  • Wakita, H. (1976). Residual energy of linear prediction applied to vowel and speaker recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 24(3), 270–271.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, South China University of Technology, No. 381 Wushan Road, Guangzhou, 510640, Guangdong Province, China

    Yan-Xiong Li & Qian-Hua He

Authors
  1. Yan-Xiong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qian-Hua He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yan-Xiong Li.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, YX., He, QH. Detecting laughter in spontaneous speech by constructing laughter bouts. Int J Speech Technol 14, 211–225 (2011). https://doi.org/10.1007/s10772-011-9097-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10772-011-9097-1

Keywords

Search

Navigation