17/05/2016 – Sander Dieleman (Google DeepMind)
05/10/2016 – Fabien Ringeval (University of Passau)
Date: Wednesday October 5 at 2 pm
Place: LORIA, room C005
Speaker: Fabien Ringeval (Université Grenoble Alpes)
Title: Affective computing from speech: towards robust recognition of emotions in ecologically valid situations
Technologies for the automatic recognition of emotion from speech have gained a significant increasing attention in the last decade, from both academic and industry, as it has found many applications in domains as various as, health care, education, serious games, brand reputation, advertisement, and robotics. Whereas good performance has been reported in the literature for acted emotions, the automatic recognition of spontaneous emotions, as expressed in ecologically valid situations, still remains an open-challenge, because such emotions are subtle, their expression and meaning depend on the speaker, the language and the culture, and they might be produced in noisy environments, which complicates the extraction of relevant cues from the speech signal. In this talk, I will present the most recent advances in the field and will show that, deep learning based methods such as long short-term memory recurrent neural networks (LSTM-RNNs), can help to contextualise relevant cues and tackle asynchrony issues for the “time- and value-continuous » prediction of emotion, but also enhance both acoustic waveform and low-level descriptors when captured in noisy conditions. Finally, I will show that, even though end-to-end learning by convolutional and LSTM-RNNs can provide promising results, they do not announce, yet, the end of signal processing for hand-engineered features extraction, as such features combined with non context-aware predictors can generalise even better than those learned by end-to-end methods, providing that they are carefully designed.
Date: Wednesday May 11 at 2 pm
Place: LORIA, room C005
Speaker: Alain Rakotomamonjy (Université de Rouen)
Title: Optimal transport for domain adaptation
Domain adaptation addresses one of the most challenging tasks in machine learning : coping with mismatch between learning and testing probability distributions. If adaptation is done correctly, models learned on a specific data representation become more robust when confronted to data depicting the same problems, but described through another observation system. Among the many strategies proposed, finding domain-invariant representations has shown excellent properties, in particular since it allows to train a unique classifier effective in all domains. In this talk, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains. We learn a transportation plan matching both PDFs, which constrains labeled samples of the same class in the source domain to remain close during transport. This way, we exploit at the same time the few labeled samples in the source domain as well as the data distributions observed in both domains. Experiments on toy and challenging real visual adaptation examples show the interest of the method, that consistently outperforms state of the art approaches. In addition, numerical experiments show that our approach leads to better performances on domain invariant deep learning features and can be easily adapted to the semi-supervised case where few labeled samples are available in the target domain.