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This paper presents an HMM-based speech-smile synthesis system. In order to do that, databases of three speech styles were recorded. This system was used to study to what extent synthesized speech-smiles (defined as Duchenne smiles in our work) and spread-lips (speech modulated by spreading the lips) communicate amusement. Our evaluation results showed that the speech-smiles synthesized sentences...
This paper presents an HMM-based synthesis approach for speechlaughs. The building stone of this project was the idea of the co-occurrence of smile and laughter bursts in varying proportions within amused speech utterances. A corpus with three complementary speaking styles was used to train the underlying HMM models: neutral speech, speech-smile, and finally laughter in different articulatory configurations...
In this paper we propose synchronization rules between acoustic and visual laughter synthesis systems. This work follows up our previous studies on acoustics laughter synthesis and visual laughter synthesis. The need of synchronization rules comes from the constraint that in laughter, HMM-based synthesis of laughter cannot be performed using a unified system where common transcriptions may be used...
In this paper we apply speaker-dependent training of Hidden Markov Models (HMMs) to audio and visual laughter synthesis separately. The two modalities are synthesized with a forced durations approach and are then combined together to render audio-visual laughter on a 3D avatar. This paper focuses on visual synthesis of laughter and its perceptive evaluation when combined with synthesized audio laughter...
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)...
In this work, we present a Hidden Markov Model (HMM) based stylistic walk synthesizer, where the synthesized styles are combinations or exaggerations of the walk styles present in the training database. In a first stage, Hidden Markov Models of eleven different styles of gait are trained, using a database of motion capture walk sequences. In a second stage, the probability density functions inside...
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