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Incremental adaptation techniques for speech recognition are aimed at adjusting acoustic models to time-variant acoustic characteristics related to such factors as changes of speaker, speaking style, and noise source over time. In this paper, we propose a novel incremental adaptation framework, which models such time-variant characteristics by successively updating posterior distributions of acoustic...
Incremental adaptation techniques for speech recognition are aimed at adjusting acoustic models quickly and stably to time-variant acoustic characteristics due to temporal changes of speaker, speaking style, noise source, etc. We proposed a novel incremental adaptation framework based on a macroscopic time evolution system, which models the time-variant characteristics by successively updating posterior...
In this paper, we reformulate an adaptation scheme of coarse/fine training (CFT) of transfer vectors in acoustic modeling by using directional statistics. In CFT, the transfer vector is decomposed into a unit direction vector and a scaling factor. By using coarse tied Gaussian class (coarse class) estimation for the unit direction vector, and by using fine tied Gaussian class (fine class) estimation...
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