Print Email Facebook Twitter Automatic discrimination between laughter and speech Title Automatic discrimination between laughter and speech Author Truong, K. van Leeuwen, D. TNO Defensie en Veiligheid Publication year 2007 Abstract Emotions can be recognized by audible paralinguistic cues in speech. By detecting these paralinguistic cues that can consist of laughter, a trembling voice, coughs, changes in the intonation contour etc., information about the speakers state and emotion can be revealed. This paper describes the development of a gender-independent laugh detector with the aim to enable automatic emotion recognition. Different types of features (spectral, prosodic) for laughter detection were investigated using different classification techniques (Gaussian Mixture Models, Support Vector Machines, Multi Layer Perceptron) often used in language and speaker recognition. Classification experiments were carried out with short pre-segmented speech and laughter segments extracted from the ICSI Meeting Recorder Corpus (with a mean duration of approximately 2 s). Equal error rates of around 3% were obtained when tested on speaker-independent speech data. We found that a fusion between classifiers based on Gaussian Mixture Models and classifiers based on Support Vector Machines increases discriminative power. We also found that a fusion between classifiers that use spectral features and classifiers that use prosodic information usually increases the performance for discrimination between laughter and speech. Our acoustic measurements showed differences between laughter and speech in mean pitch and in the ratio of the durations of unvoiced to voiced portions, which indicate that these prosodic features are indeed useful for discrimination between laughter and speech. Keywords: Automatic detection laughter; Automatic detection emotion Subject Acoustics and AudiologyAutomatic detection emotionAutomatic detection laughterAcoustic wavesError detectionGesture recognitionLearning systemsMathematical modelsAutomatic detection emotionAutomatic detection laughterLaughter segmentsSpeech dataSpeech analysisemotionsspeech recognitionautomatic speech recognitionlaughter To reference this document use: http://resolver.tudelft.nl/uuid:b3d277d6-efdd-4571-bbcb-71b991bdcd50 DOI https://doi.org/10.1016/j.specom.2007.01.001 TNO identifier 16432 Source Speech communication, 49 (2), 144-158 Document type article Files To receive the publication files, please send an e-mail request to TNO Library.