Human speech communication relies not only on audition, but also on vision, especially during poor acoustic conditions. The face is an important carrier of both linguistic and extra-linguistic information. Using computer graphics it is possible to synthesize faces and do audio-visual text-to-speech synthesis, a technique that has a number of interesting applications for example in the area of m...

We use goal babbling, a recent approach to bootstrapping inverse models, for vowel acquisition with an articulatory speech synthesizer. In contrast to motor babbling, goal babbling organizes exploration in a low-dimensional goal space. While such a goal space is naturally given in many motor learning tasks, the difficulty in modeling speech production lies within the complexity of acoustic feat...

Expressive singing voice synthesis requires an appropriate control of both prosodic and timbral aspects. While it is desirable to have an intuitive control over the expressive parameters, synthesis systems should be able to produce convincing results directly from a score. As countless interpretations of a same score are possible, the system should also target a particular singing style, which ...

We describe our progress on the construction of a combined 3D face and vocal tract simulator for articulatory speech synthesis called ArtiSynth. The architecture provides six main modules: (1) a simulator engine and synthesis framework, (2) a two and three-dimensional model development component, (3) a numerics engine, (4) a graphical renderer, (5) an audio synthesis engine and (6) a graphical ...

The goal of example-based texture synthesis methods is to generate arbitrarily large textures from limited exemplars in order to fit the exact dimensions and resolution required for a specific modeling task. The challenge is to faithfully capture all of the visual characteristics of the exemplar texture, without introducing obvious repetitions or unnatural looking visual elements. While existin...

Continuous-time Markov chains with alarms (ACTMCs) allow for alarm events that can be non-exponentially distributed. Within parametric ACTMCs, the parameters of alarm-event distributions are not given explicitly and can be subject of parameter synthesis. An algorithm solving the ε-optimal parameter synthesis problem for parametric ACTMCs with long-run average optimization objectives is presente...

In this paper, we evaluate a framework of statistical parametric speech synthesis based on Gaussian process regression (GPR) and compare it with those based on hidden Markov model (HMM) and deep neural network (DNN). Recently, for the purpose of improving the performance of HMM-based speech synthesis, novel frameworks using deep architectures have been proposed and have shown their effectivenes...

This chapter describes the application of seven approaches to support three basic task types of design (design selection, parametric design and design synthesis). The specialization of these approaches to practical design techniques is analyzed and illustrated with examples.

A statistical parametric speech synthesis system based on hidden Markov models (HMMs) has grown in popularity over the last few years. This system simultaneouslymodels spectrum, excitation, and duration of speech using context-dependent HMMs and generatesspeechwaveforms from the HMMs themselves. Since December 2002, we have publicly released an open-source software toolkit named HMM-based speec...