Automatic Phonetic Transcription of Laughter and Its Application to Laughter Synthesis

In this paper, automatic phonetic transcription of laughter is achieved with the help of Hidden Markov Models (HMMs). The models are evaluated in a speaker-independent way. Several measures to evaluate the quality of the transcriptions are discussed, some focusing on the recognized sequences (without paying attention to the segmentation of the phones), other only taking into account the segmentation boundaries (without involving the phonetic labels). Although the results are far from perfect recognition, it is shown that using this kind of automatic transcriptions does not impair too much the naturalness of laughter synthesis. The paper opens interesting perspectives in automatic laughter analysis as well as in laughter synthesis, as it will enable faster developments of laughter synthesis on large sets of laughter data.