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 …

Attention-based sequence-to-sequence (seq2seq) models have achieved promising results
in automatic speech recognition (ASR). However, as these models decode in a left-to-right …

This paper introduces a high-quality rich annotated Mandarin conversational (RAMC)
speech dataset called MagicData-RAMC. The MagicData-RAMC corpus contains 180 hours …

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 …

We present a novel large-context end-to-end automatic speech recognition (E2E-ASR)
model and its effective training method based on knowledge distillation. Common E2E-ASR …

This paper integrates a voice activity detection (VAD) function with end-to-end automatic
speech recognition toward an online speech interface and transcribing very long audio …

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 …

Typical automatic speech recognition (ASR) systems are built to recognize independent
utterances without using the cross-utterance context. However, the context over multiple …

This paper presents an approach to long-context end-to-end automatic speech recognition
(ASR) using Transformers, aiming at improving ASR accuracy for long audio recordings …