Signal Processing and Speech Communication Laboratory
homestudent projects › Classification of laughter in conversational speech

Classification of laughter in conversational speech

Master/Diploma Thesis
Seminar Type
- None -
Announcement date
08 Oct 2019
Research Areas

Short Description

In natural conversations, much of the information exchanged we actually do without using words. One of the most common nonverbal vocalizations in conversation is laughter. For instance, in business meetings 8.6% of the vocal activities are not lexical words but laughter [1]. One can expect a even higher percentage of laughter in informal conversations between friends (as the material in mind for this master thesis [3], see Figure bellow and listen to the example). Interestingly, laughter does not occur randomly in conversation and it can convey different messages (e.g., I think this is funny, I am embarrassed, you laugh so I laugh with you, etc…).

Automatic Speech Recognition systems (ASR) and Dialogue Systems so far can only distinguish laughter from regular speech; recently first attempts have been made to automatically distinguish the different meanings of laughter [2]. The main aim of this master thesis topic is to build a classifier for different kinds of laughter in conversational Austrian German.

Your Task

Review of Literature Feature extraction (e.g., using Matlab or HTK)

Building up an SVM classifier (e.g., using LIBSVM)

Evaluation of the classification.

Your Profile (recommended)

Speech Communication Laboratory

Speech Communication 1 and 2

Good Programming Skills (e.g., Python)


[1] Laskowski, K and Burger, S. (2007). Analysis of the occurrence of laughter in meetings. Interspeech 2007, pp. 1258-1261.

[2] Tanaka, H. and Campbell, N. (2014). Classification of social laughter in natural conversational speech. Computer Speech and Language 28, pp. 314-325.

[3] Schuppler, B., Hagmueller, M., Morales-Cordovilla, J. A., Pessentheiner, H.(accepted). GRASS: the Graz corpus of Read and Spontaneous Speech.

Related Research Project:Cross-layer pronunciation modeling for conversational speech (FWF Hertha Firnberg Program T572)