Evaluation of HMM-based laughter synthesis

In this paper we explore the potential of Hidden Markov Models (HMMs) for laughter synthesis. Several versions of HMMs are developed, with varying contextual information and algorithms for estimating the parameters of the source-filter synthesis model. These methods are compared, in a perceptive tests, to the naturalness of actual human laughs and copy-synthesis laughs. The evaluation shows that 1) the addition of contextual information did not increase the naturalness, 2) the proposed method is significantly less natural than human and copy-synthesized laughs, but 3) significantly improves laughter synthesis naturalness compared to the state of the art. The evaluation also demonstrates that the duration of the laughter units can be efficiently learnt by the HMM-based parametric synthesis methods.