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Bilinear models based feature space Maximum Likelihood Linear Regression (FMLLR) speaker adaptation have showed good performance for GMM-HMMs especially when the amount of adaptation data is limited. In this paper, we propose using bilinear models feature as inputs to deep neural networks (DNNs) for rapid speaker adaptation of acoustic modeling to facilitate utterance-level normalization. The effectiveness...
Bilinear models based feature space Maximum Likelihood Linear Regression (FMLLR) speaker adaptation have showed good performance especially when the amount of adaptation data is limited. However, the model dimensionality selection is very critical to the performance of bilinear models and need more work to find the optimal selection method. In this paper, we present an empirical study on this issue...
In this paper, we propose a novel method for rapid feature space Maximum Likelihood Linear Regression (FMLLR) speaker adaptation based on bilinear models. When the amount of adaptation data is limited, the conventional FMLLR transforms can be easily over-trained and can even degrade the performance. In such cases, usually by introducing structural constraints on the FMLLR transformation, the original...
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