The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

We propose Guided-TTS, a high-quality text-to-speech (TTS) model that does not require
any transcript of target speaker using classifier guidance. Guided-TTS combines an …

Large-scale automatic speech translation systems today lack key features that help machine-
mediated communication feel seamless when compared to human-to-human dialogue. In …

Audio is an essential part of our life, but creating it often requires expertise and is time-
consuming. Research communities have made great progress over the past year advancing …

Speech-to-text alignment is a critical component of neural text-to-speech (TTS) models.
Autoregressive TTS models typically use an attention mechanism to learn these alignments …

In neural text-to-speech (TTS), two-stage system or a cascade of separately learned models
have shown synthesis quality close to human speech. For example, FastSpeech2 transforms …

Recent advances in neural text-to-speech research have been dominated by two-stage
pipelines utilizing low-level intermediate speech representation such as mel-spectrograms …

While neural text-to-speech systems perform remarkably well in high-resource scenarios,
they cannot be applied to the majority of the over 6,000 spoken languages in the world due …

The task of phone-to-audio alignment has many applications in speech research. Here we
introduce two Wav2Vec2-based models for both text-dependent and text-independent …

The majority of current Text-to-Speech (TTS) datasets, which are collections of individual
utterances, contain few conversational aspects. In this paper, we introduce DailyTalk, a high …