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

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 …

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 …

The efficacy of external language model (LM) integration with existing end-to-end (E2E)
automatic speech recognition (ASR) systems can be improved significantly using the …

ASR error correction continues to serve as an important part of post-processing for speech
recognition systems. Traditionally, these models are trained with supervised training using …

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 …

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 …

Attention-based encoder-decoder (AED) models learn an implicit internal language model
(ILM) from the training transcriptions. The integration with an external LM trained on much …