Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

Conformer has proven to be effective in many speech processing tasks. It combines the
benefits of extracting local dependencies using convolutions and global dependencies …

The recently proposed Conformer model has become the de facto backbone model for
various downstream speech tasks based on its hybrid attention-convolution architecture that …

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 …

This paper introduces GigaSpeech, an evolving, multi-domain English speech recognition
corpus with 10,000 hours of high quality labeled audio suitable for supervised training, and …

Conformer, combining convolution and self-attention sequentially to capture both local and
global information, has shown remarkable performance and is currently regarded as the …

Practice and recent evidence show that state-of-the-art (SotA) automatic speech recognition
(ASR) systems do not perform equally well for all speaker groups. Many factors can cause …

Conformer-based models have become the dominant end-to-end architecture for speech
processing tasks. With the objective of enhancing the conformer architecture for efficient …

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 …