59,022 research outputs found
Articulatory features for speech-driven head motion synthesis
This study investigates the use of articulatory features for speech-driven head motion synthesis as opposed to prosody features such as F0 and energy that have been mainly used in the literature. In the proposed approach, multi-stream HMMs are trained jointly on the synchronous streams of speech and head motion data. Articulatory features can be regarded as an intermediate parametrisation of speech that are expected to have a close link with head movement. Measured head and articulatory movements acquired by EMA were synchronously recorded with speech. Measured articulatory data was compared to those predicted from speech using an HMM-based inversion mapping system trained in a semi-supervised fashion. Canonical correlation analysis (CCA) on a data set of free speech of 12 people shows that the articulatory features are more correlated with head rotation than prosodic and/or cepstral speech features. It is also shown that the synthesised head motion using articulatory features gave higher correlations with the original head motion than when only prosodic features are used. Index Terms: head motion synthesis, articulatory features, canonical correlation analysis, acoustic-to-articulatory mappin
Detecting User Engagement in Everyday Conversations
This paper presents a novel application of speech emotion recognition:
estimation of the level of conversational engagement between users of a voice
communication system. We begin by using machine learning techniques, such as
the support vector machine (SVM), to classify users' emotions as expressed in
individual utterances. However, this alone fails to model the temporal and
interactive aspects of conversational engagement. We therefore propose the use
of a multilevel structure based on coupled hidden Markov models (HMM) to
estimate engagement levels in continuous natural speech. The first level is
comprised of SVM-based classifiers that recognize emotional states, which could
be (e.g.) discrete emotion types or arousal/valence levels. A high-level HMM
then uses these emotional states as input, estimating users' engagement in
conversation by decoding the internal states of the HMM. We report experimental
results obtained by applying our algorithms to the LDC Emotional Prosody and
CallFriend speech corpora.Comment: 4 pages (A4), 1 figure (EPS
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