Unsupervised Domain Adaptation by Adversarial Learning for Robust Speech Recognition

Pavel Denisov; Ngoc Thang Vu; Marc Ferras Font

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arXiv · 1807.11284

Unsupervised Domain Adaptation by Adversarial Learning for Robust Speech Recognition

Pavel Denisov·Ngoc Thang Vu·Marc Ferras Font

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Abstract

In this paper, we investigate the use of adversarial learning for unsupervised adaptation to unseen recording conditions, more specifically, single microphone far-field speech. We adapt neural networks based acoustic models trained with close-talk clean speech to the new recording conditions using untranscribed adaptation data. Our experimental results on Italian SPEECON data set show that our proposed method achieves 19.8% relative word error rate (WER) reduction compared to the unadapted models. Furthermore, this adaptation method is beneficial even when performed on data from another language (i.e. French) giving 12.6% relative WER reduction.

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BibTeX RIS

Pavel Denisov, Ngoc Thang Vu, Marc Ferras Font. 2018-07-30. Unsupervised Domain Adaptation by Adversarial Learning for Robust Speech Recognition. https://arxiv.org/abs/1807.11284

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