Robust Speech Recognition Using Teacher-Student Learning Domain Adaptation

Adversarial Teacher-Student Learning for Unsupervised Domain Adaptation

The teacher-student (T/S) learning has been shown effective in unsupervised domain adaptation [1]. It is a form of transfer learning, not in terms of the transfer of recognition decisions, but the knowledge of posteriori probabilities in the source domain as evaluated by the teacher model. It learns to handle the speaker and environment variability inherent in and restricted to the speech signa...

Large-Scale Domain Adaptation via Teacher-Student Learning

High accuracy speech recognition requires a large amount of transcribed data for supervised training. In the absence of such data, domain adaptation of a well-trained acoustic model can be performed, but even here, high accuracy usually requires significant labeled data from the target domain. In this work, we propose an approach to domain adaptation that does not require transcriptions but ins...

Using Teacher-Student Model For Emotional Speech Recognition[In Chinese]

Domain adaptation for robust automatic speech recognition in car environments

A major obstacle for the migration of automatic speech recognition into every-day life products is environmental robustness. Automatic speech recognition systems work reasonably well under clean (laboratory) conditions but degrade seriously under real world conditions (e.g. out-door, car). A lot of research work is devoted to increase the environmental robustness of automatic speech recognition...

An unsupervised deep domain adaptation approach for robust speech recognition

This paper addresses the robust speech recognition problem as a domain adaptation task. Specifically, we introduce an unsupervised deep domain adaptation (DDA) approach to acoustic modeling in order to eliminate the training–testing mismatch that is common in real-world use of speech recognition. Under a multi-task learning framework, the approach jointly learns two discriminative classifiers u...