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- W3013079409 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." @default.
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- W3013079409 date "2018-01-01" @default.
- W3013079409 modified "2023-09-23" @default.
- W3013079409 title "Unsupervised Domain Adaptation by Adversarial Learning for Robust Speech Recognition." @default.
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