Although supervised deep learning has revolutionized speech and audio processing, it has
necessitated the building of specialist models for individual tasks and application scenarios …

This survey paper provides a comprehensive overview of the recent advancements and
challenges in applying large language models to the field of audio signal processing. Audio …

This report introduces a new family of multimodal models, Gemini, that exhibit remarkable
capabilities across image, audio, video, and text understanding. The Gemini family consists …

Modern artificial intelligence (AI) systems are powered by foundation models. This paper
presents a new set of foundation models, called Llama 3. It is a herd of language models …

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 study the capabilities of speech processing systems trained simply to predict large
amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual …

We introduce the Universal Speech Model (USM), a single large model that performs
automatic speech recognition (ASR) across 100+ languages. This is achieved by pre …

Scaling text-to-speech (TTS) to large-scale, multi-speaker, and in-the-wild datasets is
important to capture the diversity in human speech such as speaker identities, prosodies …

This paper presents XLS-R, a large-scale model for cross-lingual speech representation
learning based on wav2vec 2.0. We train models with up to 2B parameters on nearly half a …

We introduce FLEURS, the Few-shot Learning Evaluation of Universal Representations of
Speech benchmark. FLEURS is an n-way parallel speech dataset in 102 languages built on …