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Although emotional state recognition from voice has been extensively studied, there is not much effort focusing on the online emotion recognition. Since duration and intensity of emotional experiences change over time it is hard to employ existing static transition models while monitoring emotional states especially in an online setting. To overcome this difficulty we introduce a method which incorporates...
This paper presents a phonetically-aware joint density Gaussian mixture model (JD-GMM) framework for voice conversion that no longer requires parallel data from source speaker at the training stage. Considering that the phonetic level features contain text information which should be preserved in the conversion task, we propose a method that only concatenates phonetic discriminant features and spectral...
Overfitting is a commonly met issue in automatic speech recognition and is especially impacting when the amount of training data is limited. In order to address this problem, this article investigates acoustic modeling through Multi-Task Learning, with two speaker-related auxiliary tasks. Multi-Task Learning is a regularization method which aims at improving the network's generalization ability, by...
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