Advancements in deep neural networks have allowed automatic speech recognition (ASR)
systems to attain human parity on several publicly available clean speech datasets …

With the widespread adoption of deep learning, natural language processing (NLP), and
speech applications in various domains such as finance, healthcare, and government and …

The CHiME challenges have played a significant role in the development and evaluation of
robust automatic speech recognition (ASR) systems. We introduce the CHiME-7 distant ASR …

We propose an unsupervised adaptation framework, Self-TAught Recognizer (STAR), which
leverages unlabeled data to enhance the robustness of automatic speech recognition (ASR) …

Speech signals, typically sampled at rates in the tens of thousands per second, contain
redundancies, evoking inefficiencies in sequence modeling. High-dimensional speech …

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 …

Automatic speech recognition (ASR) has gained remarkable successes thanks to recent
advances of deep learning, but it usually degrades significantly under real-world noisy …

Noise robustness is essential for deploying automatic speech recognition (ASR) systems in
real-world environments. One way to reduce the effect of noise interference is to employ a …

The traditional generalized sidelobe canceller (GSC) is a common speech enhancement
front end to improve the noise robustness of automatic speech recognition (ASR) systems in …

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 …