Deepfake audio as a data augmentation technique for training automatic
  speech to text transcription models
Alexandre R. Ferreira Cláudio E. C. Campelo
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.