Abstract Pretrained Foundation Models (PFMs) are regarded as the foundation for various
downstream tasks across different data modalities. A PFM (eg, BERT, ChatGPT, GPT-4) is …

Nowadays speech synthesis or text to speech (TTS), an ability of system to produce human
like natural sounding voice from the written text, is gaining popularity in the field of speech …

We introduce a language modeling approach for text to speech synthesis (TTS). Specifically,
we train a neural codec language model (called Vall-E) using discrete codes derived from …

Text to speech (TTS), or speech synthesis, which aims to synthesize intelligible and natural
speech given text, is a hot research topic in speech, language, and machine learning …

This paper introduces a new speech corpus called" LibriTTS" designed for text-to-speech
use. It is derived from the original audio and text materials of the LibriSpeech corpus, which …

This paper proposes a novel unsupervised autoregressive neural model for learning generic
speech representations. In contrast to other speech representation learning methods that …

Learning meaningful and general representations from unannotated speech that are
applicable to a wide range of tasks remains challenging. In this paper we propose to use …

Speech synthesis, also known as text-to-speech (TTS), has attracted increasingly more
attention. Recent advances on speech synthesis are overwhelmingly contributed by deep …

This paper proposes a hierarchical, fine-grained and interpretable latent variable model for
prosody based on the Tacotron 2 text-to-speech model. It achieves multi-resolution …

Speech synthesis (text to speech, TTS) and recognition (automatic speech recognition, ASR)
are important speech tasks, and require a large amount of text and speech pairs for model …