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Unsupervised Domain Adaptation Schemes for Building ASR in Low-Resource Languages

Anoop, CS and Prathosh, AP and Ramakrishnan, AG (2021) Unsupervised Domain Adaptation Schemes for Building ASR in Low-Resource Languages. In: 2021 IEEE Automatic Speech Recognition and Understanding Workshop, 13 - 17 December 2021, Cartagena, pp. 342-349.

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Official URL: https://doi.org/10.1109/ASRU51503.2021.9688269


Building an automatic speech recognition (ASR) system from scratch requires a large amount of annotated speech data, which is difficult to collect in many languages. However, there are cases where the low-resource language shares a common acoustic space with a high-resource language having enough annotated data to build an ASR. In such cases, we show that the domain-independent acoustic models learned from the high-resource language through unsupervised domain adaptation (UDA) schemes can enhance the performance of the ASR in the low-resource language. We use the specific example of Hindi in the source domain and Sanskrit in the target domain. We explore two architectures: i) domain adversarial training using gradient reversal layer (GRL) and ii) domain separation networks (DSN). The GRL and DSN architectures give absolute improvements of 6.71 and 7.32, respectively, in word error rate over the baseline deep neural network model when trained on just 5.5 hours of data in the target domain. We also show that choosing a proper language (Telugu) in the source domain can bring further improvement. The results suggest that UDA schemes can be helpful in the development of ASR systems for low-resource languages, mitigating the hassle of collecting large amounts of annotated speech data.

Item Type: Conference Paper
Publication: 2021 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2021 - Proceedings
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Deep neural networks; Multilayer neural networks; Network architecture; Speech, Adaptation scheme; Automatic speech recognition; Automatic speech recognition system; Domain adaptation; Domain separation network; Large amounts; Low resource languages; Separation network; Speech data; Unsupervised domain adaptation, Speech recognition
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 23 May 2023 03:41
Last Modified: 23 May 2023 03:41
URI: https://eprints.iisc.ac.in/id/eprint/81717

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