3 accepted papers
Synthetic speech is a useful source for augmentation of automatic speech recognition (ASR) systems, but there is a "sim-to-real" gap between synthetic and real speech that can limit generalization. The natural variability of real speech is essential to the training of robust ASR systems. While synth…
Collecting high-quality studio recordings of audio is challenging, which limits the language coverage of text-to-speech (TTS) systems. This paper proposes a framework for scaling a multilingual TTS model to 100+ languages using found data without supervision. The proposed framework combines speech-t…
Although neural end-to-end text-to-speech models can synthesize highly natural speech, there is still room for improvements to its efficiency and naturalness. This paper proposes a non-autoregressive neural text-to-speech model augmented with a variational autoencoder-based residual encoder. This mo…