activity
20192022
most citedTransformer-based Online CTC/attention End-to-End Speech Recognition Architecture

3 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2022

Summary on the ISCSLP 2022 Chinese-English Code-Switching ASR Challenge

Shuhao Deng, Chengfei Li, Jinfeng Bai +6

Code-switching automatic speech recognition becomes one of the most challenging and the most valuable scenarios of automatic speech recognition, due to the code-switching phenomeno…

cs.CL2022

Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset

Zehui Yang, Yifan Chen, Lei Luo +9

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

cs.CL2022

Improving CTC-based speech recognition via knowledge transferring from pre-trained language models

Keqi Deng, Songjun Cao, Yike Zhang +4

Recently, end-to-end automatic speech recognition models based on connectionist temporal classification (CTC) have achieved impressive results, especially when fine-tuned from wav2…

eess.AS2022

Improving non-autoregressive end-to-end speech recognition with pre-trained acoustic and language models

Keqi Deng, Zehui Yang, Shinji Watanabe +3

While Transformers have achieved promising results in end-to-end (E2E) automatic speech recognition (ASR), their autoregressive (AR) structure becomes a bottleneck for speeding up…

eess.AS20203 cited

Transformer-based Online CTC/attention End-to-End Speech Recognition Architecture

Haoran Miao, Gaofeng Cheng, Changfeng Gao +2

Recently, Transformer has gained success in automatic speech recognition (ASR) field. However, it is challenging to deploy a Transformer-based end-to-end (E2E) model for online spe…

cs.SD20191 cited

Utterance-level Permutation Invariant Training with Latency-controlled BLSTM for Single-channel Multi-talker Speech Separation

Lu Huang, Gaofeng Cheng, Pengyuan Zhang +3

Utterance-level permutation invariant training (uPIT) has achieved promising progress on single-channel multi-talker speech separation task. Long short-term memory (LSTM) and bidir…