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Linear discriminant analysis (LDA) and Gaussian probabilistic LDA (PLDA) have been shown to effectively suppress channel- and session-variability of i-vectors. But they suffer the following limitations: 1) In LDA, a single linear transformation may not be adequate to describe the nonlinear relationship of features and 2) Gaussian-PLDA assumes the speaker and channel factors follow a Gaussian distribution,...
In this paper, we propose a feature adaptation method that combines speech features from multiple microphone channels for robust automatic speech recognition (ASR). The proposed method first transforms the features in all channels using channel-dependent linear transforms, and then sum the channels into one channel for acoustic modeling. The transform parameters are estimated by maximizing the likelihood...
Spectral information represents short-term speech information within a frame of a few tens of milliseconds, while temporal information captures the evolution of speech statistics over consecutive frames. Motivated by the findings that human speech comprehension relies on the integrity of both the spectral content and temporal envelope of speech signal, we study a spectro-temporal transform framework...
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