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This paper explores statistical tools that help analyze the predictability in the acoustic-to-articulatory inversion of speech, using an Electromagnetic Articulography database of simultaneously recorded acoustic and articulatory data. Since it has been shown that speech acoustics can be mapped to non-unique articulatory modes, the variance of the articulatory parameters is not sufficient to understand...
Segmentation of speech signals is a crucial task in many types of speech analysis. We present a novel approach at segmentation on a syllable level, using a Bidirectional Long-Short-Term Memory Neural Network. It performs estimation of syllable nucleus positions based on regression of perceptually motivated input features to a smooth target function. Peak selection is performed to attain valid nuclei...
When human listeners utter Listener Responses (e.g. back-channels or acknowledgments) such as ‘yeah’ and ‘mmhmm’, interlocutors commonly continue to speak or resume their speech even before the listener has finished his/her response. This type of speech interactivity results in frequent speech overlap which is common in human-human conversation. To allow for this type of speech interactivity to occur...
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