Factorized context modelling for Text-to-Speech synthesis

Because speech units are so context-dependent, a large number of linguistic context features are generally used by HMMbased Text-to-Speech (TTS) speech synthesis systems, via context-dependent models. Since it is impossible to train separate models for every context, decision trees are used to discover the most important combinations of features that should be modelled. The task of the decision tree is very hard- to generalize from a very small observed part of the context feature space to the rest- and they have a major weakness: they cannot directly take advantage of factorial properties: they subdivide the model space based on one feature at a time. We propose a Dynamic Bayesian Network (DBN) based Mixed Memory Markov Model (MMMM) to provide factorization of the context space. The results of a listening test are provided as evidence that the model successfully learns the factorial nature of this space

Towards responsive Sensitive Artificial Listeners

This paper describes work in the recently started project SEMAINE, which aims to build a set of Sensitive Artificial Listeners – conversational agents designed to sustain an interaction with a human user despite limited verbal skills, through robust recognition and generation of non-verbal behaviour in real-time, both when the agent is speaking and listening. We report on data collection and on the design of a system architecture in view of real-time responsiveness

Generative Image Modeling Using Spatial LSTMs

Modeling the distribution of natural images is challenging, partly because of strong statistical dependencies which can extend over hundreds of pixels. Recurrent neural networks have been successful in capturing long-range dependencies in a number of problems but only recently have found their way into generative image models. We here introduce a recurrent image model based on multi-dimensional long short-term memory units which are particularly suited for image modeling due to their spatial structure. Our model scales to images of arbitrary size and its likelihood is computationally tractable. We find that it outperforms the state of the art in quantitative comparisons on several image datasets and produces promising results when used for texture synthesis and inpainting

Twin Networks: Matching the Future for Sequence Generation

We propose a simple technique for encouraging generative RNNs to plan ahead. We train a "backward" recurrent network to generate a given sequence in reverse order, and we encourage states of the forward model to predict cotemporal states of the backward model. The backward network is used only during training, and plays no role during sampling or inference. We hypothesize that our approach eases modeling of long-term dependencies by implicitly forcing the forward states to hold information about the longer-term future (as contained in the backward states). We show empirically that our approach achieves 9% relative improvement for a speech recognition task, and achieves significant improvement on a COCO caption generation task.Comment: 12 pages, 3 figures, published at ICLR 201