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This paper deals with the adaptation of classification model in the binary mask approach to suppress noise in the noisy environment. The binary mask estimation approach is known to improve speech intelligibility of noisy speech. However, the same type of noisy data for the test data should be included in the training data for building the classification model of binary mask estimation. The eigenvoice adaptation is applied to the noise-independent classification model and the adapted model is used as noise-dependent model. The results are reported in Hit rates and False alarm rates. The experimental results confirmed that the accuracy of classification is improved as the number of adaptation sentences increases.

Eigenvoice Adaptation of Classification Model for Binary Mask Estimation