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 …

We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

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 …

We investigate a set of techniques for RNN Transducers (RNN-Ts) that were instrumental in
lowering the word error rate on three different tasks (Switchboard 300 hours, conversational …

The integration of Language Models (LMs) has proven to be an effective way to address
domain shifts in speech recognition. However, these approaches usually require a …

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 …

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 …

We present JOIST, an algorithm to train a streaming, cascaded, encoder end-to-end (E2E)
model with both speech-text paired inputs, and text-only unpaired inputs. Unlike previous …

End-to-end (E2E) automatic speech recognition (ASR) models have recently demonstrated
superior performance over the traditional hybrid ASR models. Training an E2E ASR model …

Previous works on the Recurrent Neural Network-Transducer (RNN-T) models have shown
that, under some conditions, it is possible to simplify its prediction network with little or no …