Expanding the language coverage of speech technology has the potential to improve
access to information for many more people. However, current speech technology is …

We introduce AudioLM, a framework for high-quality audio generation with long-term
consistency. AudioLM maps the input audio to a sequence of discrete tokens and casts …

Abstract Automatic Speech Recognition (ASR) systems can be trained to achieve
remarkable performance given large amounts of manually transcribed speech, but large …

Self-supervised learning in speech involves training a speech representation network on a
large-scale unannotated speech corpus, and then applying the learned representations to …

We introduce Moshi, a speech-text foundation model and full-duplex spoken dialogue
framework. Current systems for spoken dialogue rely on pipelines of independent …

Recent progress in self-supervised or unsupervised machine learning has opened the
possibility of building a full speech processing system from raw audio without using any …

Contrastive predictive coding (CPC) aims to learn representations of speech by
distinguishing future observations from a set of negative examples. Previous work has …

Recent work in spoken language modeling shows the possibility of learning a language
unsupervisedly from raw audio without any text labels. The approach relies first on …

Self-supervised learning (SSL) for speech representation has been successfully applied in
various downstream tasks, such as speech and speaker recognition. More recently, speech …

Indonesia is home to roughly 700 languages, which amounts to about ten percent of the
global total, positioning it as the second-most linguistically diverse country after Papua New …