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 …

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …

We present a simple and efficient auxiliary loss function for automatic speech recognition
(ASR) based on the connectionist temporal classification (CTC) objective. The proposed …

Transformers have recently dominated the ASR field. Although able to yield good
performance, they involve an autoregressive (AR) decoder to generate tokens one by one …

Very deep transformers outperform conventional bi-directional long short-term memory
networks for automatic speech recognition (ASR) by a significant margin. However, being …

In recent years, extensive research has been conducted on the vulnerability of ASR systems,
revealing that black-box adversarial example attacks pose significant threats to real-world …

This paper proposes a method to relax the conditional independence assumption of
connectionist temporal classification (CTC)-based automatic speech recognition (ASR) …

Attention-based encoder-decoder (AED) models have achieved promising performance in
speech recognition. However, because the decoder predicts text tokens (such as characters …

Abstract English language education is undergoing a transformative shift, propelled by
advancements in technology. This research explores the integration of the Internet of Things …