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 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 …

Although audio generation shares commonalities across different types of audio, such as
speech, music, and sound effects, designing models for each type requires careful …

Self-supervised learning (SSL) achieves great success in speech recognition, while limited
exploration has been attempted for other speech processing tasks. As speech signal …

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …

In this paper, we present WenetSpeech, a multi-domain Mandarin corpus consisting of
10000+ hours high-quality labeled speech, 2400+ hours weakly labeled speech, and about …

We present the latest iteration of the voice conversion challenge (VCC) series, a bi-annual
scientific event aiming to compare and understand different voice conversion (VC) systems …

This paper presents FunCodec, a fundamental neural speech codec toolkit, which is an
extension of the open-source speech processing toolkit FunASR. FunCodec provides …

The impressive capability and versatility of large language models (LLMs) have aroused
increasing attention in automatic speech recognition (ASR), with several pioneering studies …