With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models.
In this paper, we present Synt++, an improved algorithm to utilize synthetic data for training speech recognition models. Synt++ combines two novel techniques.
Tjandra et al. [9] investigated the combination of end-to-end ASR and TTS to achieve a deep learning-based speech chain model. In this work, we focus on a ...
Oct 21, 2021 · This work proposes two novel techniques during training to mitigate the problems due to the distribution gap in speech recognition models ...
May 30, 2023 – This paper proposes a method for selecting synthetic speech samples from a large text-to-speech dataset to improve the training of ...
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With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models. However, machine ...
Synt++: Utilizing imperfect synthetic data to improve speech recognition. TY ... Text is all you need: Personalizing ASR models using controllable speech ...
With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models. Automatic Speech ...
Jun 20, 2024 · Synthetic data plays a crucial role in enhancing human speech analysis by providing a viable alternative to real data for training speech ...
With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models. However, machine ...
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