Deepfake audio as a data augmentation technique for training automatic speech to text transcription models

• URL: http://arxiv.org/abs/2309.12802v1
• Date: Fri, 22 Sep 2023 11:33:03 GMT
• Title: Deepfake audio as a data augmentation technique for training automatic speech to text transcription models
• Authors: Alexandre R. Ferreira, Cl\'audio E. C. Campelo
• Abstract summary: We propose a framework that approaches data augmentation based on deepfake audio. A dataset produced by Indians (in English) was selected, ensuring the presence of a single accent.
• Score: 55.2480439325792
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: To train transcriptor models that produce robust results, a large and diverse labeled dataset is required. Finding such data with the necessary characteristics is a challenging task, especially for languages less popular than English. Moreover, producing such data requires significant effort and often money. Therefore, a strategy to mitigate this problem is the use of data augmentation techniques. In this work, we propose a framework that approaches data augmentation based on deepfake audio. To validate the produced framework, experiments were conducted using existing deepfake and transcription models. A voice cloner and a dataset produced by Indians (in English) were selected, ensuring the presence of a single accent in the dataset. Subsequently, the augmented data was used to train speech to text models in various scenarios.

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