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Recent advances in automatic speech recognition have used
large corpora and powerful computational resources to train
complex statistical models from high-dimensional features, to
attempt to capture all the variability found in natural speech.
Such models are difficult to interpret and may be fragile, and
contradict or ignore knowledge of human speech produc-
tion and perception. We report progress towards phoneme
recognition using a model of speech which employs very few
parameters and which is more faithful to the dynamics and

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In this paper, we present our work on speech-smile/shaking vowels classification. An efficient classification system would be a first step towards the estimation (from speech signals only) of amusement levels beyond smile, as indeed shaking vowels represent a transition from smile to laughter superimposed to speech. A database containing examples of both classes has been collected from acted and spontaneous speech corpora. An experimental study using several acoustic feature sets is presented here, and novel features are also proposed.

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