Transfer Learning for Speech and Language Processing

Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language, with little or no re-training data. Transfer learning is closely related to multi-task learning (cross-lingual vs. multilingual), and is traditionally studied in the name of `model adaptation'. Recent advance in deep learning shows that transfer learning becomes much easier and more effective with high-level abstract features learned by deep models, and the `transfer' can be conducted not only between data distributions and data types, but also between model structures (e.g., shallow nets and deep nets) or even model types (e.g., Bayesian models and neural models). This review paper summarizes some recent prominent research towards this direction, particularly for speech and language processing. We also report some results from our group and highlight the potential of this very interesting research field.Comment: 13 pages, APSIPA 201

Investigating multisensory integration in emotion recognition through bio-inspired computational models

Emotion understanding represents a core aspect of human communication. Our social behaviours are closely linked to expressing our emotions and understanding others emotional and mental states through social signals. The majority of the existing work proceeds by extracting meaningful features from each modality and applying fusion techniques either at a feature level or decision level. However, these techniques are incapable of translating the constant talk and feedback between different modalities. Such constant talk is particularly important in continuous emotion recognition, where one modality can predict, enhance and complement the other. This paper proposes three multisensory integration models, based on different pathways of multisensory integration in the brain; that is, integration by convergence, early cross-modal enhancement, and integration through neural synchrony. The proposed models are designed and implemented using third-generation neural networks, Spiking Neural Networks (SNN). The models are evaluated using widely adopted, third-party datasets and compared to state-of-the-art multimodal fusion techniques, such as early, late and deep learning fusion. Evaluation results show that the three proposed models have achieved comparable results to the state-of-the-art supervised learning techniques. More importantly, this paper demonstrates plausible ways to translate constant talk between modalities during the training phase, which also brings advantages in generalisation and robustness to noise.PostprintPeer reviewe