Self-supervised approaches for speech representation learning are challenged by three
unique problems:(1) there are multiple sound units in each input utterance,(2) there is no …

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 …

Compared to vision and language applications, self-supervised pre-training approaches for
ASR are challenged by three unique problems:(1) There are multiple sound units in each …

This paper proposes an efficient memory transformer Emformer for low latency streaming
speech recognition. In Emformer, the long-range history context is distilled into an …

We propose and evaluate transformer-based acoustic models (AMs) for hybrid speech
recognition. Several modeling choices are discussed in this work, including various …

How to leverage dynamic contextual information in end-to-end speech recognition has
remained an active research area. Previous solutions to this problem were either designed …

End-to-end models in general, and Recurrent Neural Network Transducer (RNN-T) in
particular, have gained significant traction in the automatic speech recognition community in …

There is a growing interest in the speech community in developing Recurrent Neural
Network Transducer (RNN-T) models for automatic speech recognition (ASR) applications …

Transformer-based acoustic modeling has achieved great suc-cess for both hybrid and
sequence-to-sequence speech recogni-tion. However, it requires access to the full …

End-to-end automatic speech recognition (ASR) models with a single neural network have
recently demonstrated state-of-the-art results compared to conventional hybrid speech …