Unsupervised Domain Adaptation in Speech Recognition using Phonetic Features release_mp4prfyp4repvbjtaf2qwy7qmu

by Rupam Ojha, C Chandra Sekhar

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2021  

Abstract

Automatic speech recognition is a difficult problem in pattern recognition because several sources of variability exist in the speech input like the channel variations, the input might be clean or noisy, the speakers may have different accent and variations in the gender, etc. As a result, domain adaptation is important in speech recognition where we train the model for a particular source domain and test it on a different target domain. In this paper, we propose a technique to perform unsupervised gender-based domain adaptation in speech recognition using phonetic features. The experiments are performed on the TIMIT dataset and there is a considerable decrease in the phoneme error rate using the proposed approach.
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Date   2021-08-04
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arXiv  2108.02850v1
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