Efficient acoustic feature transformation in mismatched environments using a Guided-GAN

• URL: http://arxiv.org/abs/2210.00721v3
• Date: Thu, 6 Oct 2022 06:33:38 GMT
• Title: Efficient acoustic feature transformation in mismatched environments using a Guided-GAN
• Authors: Walter Heymans, Marelie H. Davel, Charl van Heerden
• Abstract summary: We propose a new framework to improve automatic speech recognition systems in resource-scarce environments. We use a generative adversarial network (GAN) operating on acoustic input features to enhance the features of mismatched data. With less than one hour of data, an ASR system trained on good quality data, and evaluated on mismatched audio is improved by between 11.5% and 19.7% relative word error rate (WER)
• Score: 1.495380389108477
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We propose a new framework to improve automatic speech recognition (ASR) systems in resource-scarce environments using a generative adversarial network (GAN) operating on acoustic input features. The GAN is used to enhance the features of mismatched data prior to decoding, or can optionally be used to fine-tune the acoustic model. We achieve improvements that are comparable to multi-style training (MTR), but at a lower computational cost. With less than one hour of data, an ASR system trained on good quality data, and evaluated on mismatched audio is improved by between 11.5% and 19.7% relative word error rate (WER). Experiments demonstrate that the framework can be very useful in under-resourced environments where training data and computational resources are limited. The GAN does not require parallel training data, because it utilises a baseline acoustic model to provide an additional loss term that guides the generator to create acoustic features that are better classified by the baseline.

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