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We focus on the problem of speech recognition in the presence of nonstationary sudden noise, which is very likely to happen in home environments. To handle this problem, a model compensation method based on a factorial hidden Markov model (FHMM) has been recently introduced. In this architecture, speech and noise processes are modeled in parallel by a phoneme FHMM that is built by combining a clean-speech...
We propose an acoustic model training method which combines committee-based active learning and semi-supervised learning for large vocabulary continuous speech recognition. In this method, each untranscribed training utterance is examined by a committee of multiple speech recognizers, and the degree of disagreement in the committee on its transcription is used for selecting utterances. Those utterances...
We propose Cross-Channel Spectral Subtraction (CCSS), a source separation method for recognizing meeting speech where one microphone is prepared for each speaker. The method quickly adapts to changes in transfer functions and uses spectral subtraction to suppress the speech of other speakers. Compared with conventional source separation methods based on independent component analysis (ICA) or that...
In recent years, adaptation techniques have been given special focus in speaker recognition tasks, mainly targeting speaker and session variation disentangling under the Maximum a Posteriori (MAP) criterion. For these techniques, unseen mixtures are usually adapted in a global manner, if ever. In this paper, we explore Structural MAP (SMAP), Maximum a Posteriori adaptation using hierarchical structures...
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