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Gaussian mixture model (GMM) based speech-to-lips conversion often operates in two alternative ways: batch conversion and sliding window-based conversion for real-time processing. Previously, Minimum Converted Trajectory Error (MCTE) training has been proposed to improve the performance of batch conversion. In this paper, we extend previous work and propose a new training criteria, MCTE for Real-time...
Advances in speech-processing technology have enabled novel ways to learn a foreign language online. With Engkoo, researchers in China are working to turn any computer into a language learning assistant and make searching a language easier. The featured Web extra at http://youtu.be/_VHDMAKKLKo is a video discussion titled “Computer-Assisted Audiovisual Language Learning” that demonstrates Engkoo,...
In this paper, we propose a minimum generation error (MGE) training method to refine the audio-visual HMM to improve visual speech trajectory synthesis. Compared with the traditional maximum likelihood (ML) estimation, the proposed MGE training explicitly optimizes the quality of generated visual speech trajectory, where the audio-visual HMM modeling is jointly refined by using a heuristic method...
In this paper, we propose an HMM trajectory-guided, real image sample concatenation approach to photo-real talking head synthesis. An audio-visual database of a person is recorded first for training a statistical Hidden Markov Model (HMM) of Lips movement. The HMM is then used to generate the dynamic trajectory of lips movement for given speech signals in the maximum probability sense. The generated...
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