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 …

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 …

End-to-end (E2E) models have made rapid progress in automatic speech recognition (ASR)
and perform competitively relative to conventional models. To further improve the quality, a …

Text-only adaptation of a transducer model remains challenging for end-to-end speech
recognition since the transducer has no clearly separated acoustic model (AM), language …

The applications of automatic speech recognition (ASR) systems are proliferating, in part
due to recent significant quality improvements. However, as recent work indicates, even …

Interactive speech recognition systems must generate words quickly while also producing
accurate results. Two-pass models excel at these requirements by employing a first-pass …

While there have been several contributions exploring state of the art techniques for text
normalization, the problem of inverse text normalization (ITN) remains relatively unexplored …

In this paper, several works are proposed to address practi-cal challenges for deploying
RNN Transducer (RNN-T) based speech recognition systems. These challenges are …

Recently, we have witnessed excellent improvement of end-to-end (E2E) automatic speech
recognition (ASR). However, how to tackle the long-tailed data distribution problem while …

End-to-end models are an attractive new approach to spoken language understanding
(SLU) in which the meaning of an utterance is inferred directly from the raw audio, without …