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 …

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

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 …

The massive growth of self-supervised learning (SSL) has been witnessed in language,
vision, speech, and audio domains over the past few years. While discrete label prediction is …

We introduce AudioPaLM, a large language model for speech understanding and
generation. AudioPaLM fuses text-based and speech-based language models, PaLM-2 [Anil …

We present SoundStorm, a model for efficient, non-autoregressive audio generation.
SoundStorm receives as input the semantic tokens of AudioLM, and relies on bidirectional …

What does it take to create the Babel Fish, a tool that can help individuals translate speech
between any two languages? While recent breakthroughs in text-based models have …

Large language models (LLMs) have proven themselves highly flexible, able to solve a wide
range of generative tasks, such as abstractive summarization and open-ended question …

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 …

Abstract We propose Conditional Adapter (CoDA), a parameter-efficient transfer learning
method that also improves inference efficiency. CoDA generalizes beyond standard adapter …