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 …

The remarkable success of transformers in the field of natural language processing has
sparked the interest of the speech-processing community, leading to an exploration of their …

Today, the Transformer model, which allows parallelization and also has its own internal
attention, has been widely used in the field of speech recognition. The great advantage of …

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

This paper addresses end-to-end automatic speech recognition (ASR) for long audio
recordings such as lecture and conversational speeches. Most end-to-end ASR models are …

Speech summarization is typically performed by using a cascade of speech recognition and
text summarization models. End-to-end modeling of speech summarization models is …

Long-content automatic speech recognition (ASR) has obtained increasing interest in recent
years, as it captures the relationship among consecutive historical utterances while …

Automatic Speech Recognition (ASR) in conversational settings presents unique
challenges, including extracting relevant contextual information from previous …

Current ASR systems are mainly trained and evaluated at the utterance level. Long range
cross utterance context can be incorporated. A key task is to derive a suitable compact …

This paper presents the development of language tutoring systems for non‐native speakers
by leveraging advanced end‐to‐end automatic speech recognition (ASR) and proficiency …