Zhaobing Han

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Automatic speech recognition in telecommunications environment still has a lower correct rate compared to its desktop pairs. Improving the performance of telephone-quality speech recognition is an urgent problem for its application in those practical fields. Previous works have shown that the main reason for this performance degradation is the variational(More)
Adapting the parameters of a statistical speaker independent continuous speech recognizer to the speaker can significantly improve the recognition performance and robustness of the system. In this paper, we propose a novel target-driven speaker adaptation method, Generalized Joint Maximum a Posteriori (GJMAP), which extends and improves the previous(More)
Robust estimate of a large number of parameters against the availability of training data is a crucial issue in triphone based continuous speech recognition. To cope with the issue, two major context-clustering methods, agglomerative (AGG) and tree-based (TB), have been widely studied. In this paper, we analyze two algorithms with respect to their(More)
1 The degradation of speech recognition performance in real-life environments and through transmission channels is a main embarrassment for many speech-based applications around the world, especially when non-stationary noise and changing channel exist. In this paper, we extend our previous works on Maximum-Likelihood (ML) dynamic channel compensation by(More)
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