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 …

Recently Transformer and Convolution neural network (CNN) based models have shown
promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural …

We present a simple and effective self-supervised learning approach for speech recognition.
The approach learns a model to predict the masked speech signals, in the form of discrete …

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 …

Convolutional neural networks (CNN) have shown promising results for end-to-end speech
recognition, albeit still behind other state-of-the-art methods in performance. In this paper …

Recently, we made available WeNet, a production-oriented end-to-end speech recognition
toolkit, which introduces a unified two-pass (U2) framework and a built-in runtime to address …

End-to-end (E2E) models have shown to outperform state-of-the-art conventional models for
streaming speech recognition [1] across many dimensions, including quality (as measured …

Recently, there has been a strong push to transition from hybrid models to end-to-end (E2E)
models for automatic speech recognition. Currently, there are three promising E2E methods …

The external language models (LM) integration remains a challenging task for end-to-end
(E2E) automatic speech recognition (ASR) which has no clear division between acoustic …

Because of its streaming nature, recurrent neural network transducer (RNN-T) is a very
promising end-to-end (E2E) model that may replace the popular hybrid model for automatic …