The analysis of 3D scenes consisting of nonrigid moving objects from 2D image sequences is discussed. A parametric description of dynamic objects is extracted and the time variant scene parameters are estimated throughout the sequence by employing an analysis by synthesis approach. From the parametric scene description images are synthesized and compared with the original input images of the ca...

Statistical parametric speech synthesis, based on HMM-like models, has become competitive with established concatenative techniques over the last few years. This paper offers a non-mathematical introduction to this method of speech synthesis. It is intended to be complementary to the wide range of excellent technical publications already available. Rather than offer a comprehensive literature r...

Bidirectional recurrent neural nets have demonstrated state-ofthe-art performance for parametric speech synthesis. In this paper, we introduce a top-down application of recurrent neural net models to unit-selection synthesis. A hierarchical cascaded network graph predicts context phone duration, speech unit encoding and frame-level logF0 information that serves as targets for the search of unit...

This paper presents an interactive tool for working with parametric speech synthesis. The tool facilitates editing of parameter tracks, and it provides real-time feedback of the synthesised speech. It serves as an interface to KTH’s multilingual rule based synthesis system, and can be used to control a formant synthesiser as well as a 3-D “talking head”. The tool has been used in research and e...

This paper proposes a novel technique for speaker and language adaptive training for HMM-based statistical parametric polyglot speech synthesis. Language-specific context-dependencies in the system are captured using CAT with cluster-dependent decision trees. Acoustic variations caused by speaker characteristics are handled by CMLLR-based transforms. This framework allows multi-speaker/multi-la...

We study the synthesis problem for distributed architectures with a parametric number of finite-state components. Parameterized specifications arise naturally in a synthesis setting, but thus far it was unclear how to decide realizability and how to perform synthesis. Using a classical result from verification, we show that for specifications in LTL\X, parameterized synthesis of token ring netw...

Hidden Markov model (HMM)-based parametric speech synthesis has become a mainstream speech synthesis method in recent years. This method is able to synthesise highly intelligible and smooth speech sounds. In addition, it makes speech synthesis far more flexible compared to the conventional unit selection and waveform concatenation approach. Several adaptation and interpolation methods have been...

The robust control system objects with intervalundermined parameters is considers in this paper. Initial information about the system is its characteristic polynomial with interval coefficients. On the basis of coefficient estimations of quality indices and criterion of the maximum stability degree, the methods of synthesis of a robust regulator parametric is developed. The example of the robus...

We study finite-state controllers (FSCs) for partially observable Markov decision processes (POMDPs). The key insight is that computing (randomized) FSCs on POMDPs is equivalent to synthesis for parametric Markov chains (pMCs). This correspondence enables using parameter synthesis techniques to compute FSCs for POMDPs in a black-box fashion. We investigate how typical restrictions on parameter ...

In hidden Markov model-based unit selection synthesis, the benefits of both unit selection and statistical parametric speech synthesis are combined. However, conventional Viterbi algorithm is forced to do a selection also when no suitable units are available. This can drift the search and decrease the overall quality. Consequently, we propose to use robust Viterbi algorithm that can simultaneou...