Recent advances in large language models (LLMs) have promoted generative error
correction (GER) for automatic speech recognition (ASR), which leverages the rich linguistic …

Speech enhancement (SE) aims to suppress the additive noise from noisy speech signals to
improve the speech's perceptual quality and intelligibility. However, the over-suppression …

Speech enhancement (SE) is proved effective in reducing noise from noisy speech signals
for downstream automatic speech recognition (ASR), where multi-task learning strategy is …

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 …

This paper addresses the training issues associated with neural network-based automatic
speech recognition (ASR) under noise conditions. In particular, conventional joint training …

Recent studies in neural network-based monaural speech separation (SS) have achieved a
remarkable success thanks to increasing ability of long sequence modeling. However, they …

Underwater acoustic signal recognition (UASR) systems face challenges in achieving high
accuracy when processing complex data with low signal-to-noise ratio (SNR) in underwater …

Recently, speech enhancement (SE) methods have achieved quite good performances.
However, because of the speech distortion problem, the enhanced speech may lose …

Automatic speech recognition (ASR) systems degrade significantly under noisy conditions.
Recently, speech enhancement (SE) is introduced as front-end to reduce noise for ASR, but …

To realize robust End-to-end Automatic Speech Recognition (E2E ASR) under radio
communication condition, we propose a multitask-based method to jointly train a Speech …