The recent advancements in large language models (LLMs) have revolutionized the field of
natural language processing, progressively broadening their scope to multimodal …

We present MELLE, a novel continuous-valued tokens based language modeling approach
for text to speech synthesis (TTS). MELLE autoregressively generates continuous mel …

This paper introduces Embarrassingly Easy Text-to-Speech (E2 TTS), a fully non-
autoregressive zero-shot text-to-speech system that offers human-level naturalness and …

Building on the foundations of language modeling in natural language processing, Next
Token Prediction (NTP) has evolved into a versatile training objective for machine learning …

This paper introduces F5-TTS, a fully non-autoregressive text-to-speech system based on
flow matching with Diffusion Transformer (DiT). Without requiring complex designs such as …

Understanding the speaking style, such as the emotion of the interlocutor's speech, and
responding with speech in an appropriate style is a natural occurrence in human …

Full-duplex multimodal large language models (LLMs) provide a unified framework for
addressing diverse speech understanding and generation tasks, enabling more natural and …

Automatic speech recognition (ASR) systems have become increasingly popular in recent
years due to their ability to convert spoken language into text. Nonetheless, despite their …

We introduce a text-to-speech (TTS) model called BASE TTS, which stands for $\textbf {B} $
ig $\textbf {A} $ daptive $\textbf {S} $ treamable TTS with $\textbf {E} $ mergent abilities …

Multimodal generative models require a unified approach to handle both discrete data (eg,
text and code) and continuous data (eg, image, audio, video). In this work, we propose …