activity
20212024
most citedFastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition

30 citations · 41 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CL2024★ 2 cited

WavLLM: Towards Robust and Adaptive Speech Large Language Model

Shujie Hu, Long Zhou, Shujie Liu +9

The recent advancements in large language models (LLMs) have revolutionized the field of natural language processing, progressively broadening their scope to multimodal perception…

eess.AS2023★ 3 cited

On decoder-only architecture for speech-to-text and large language model integration

Jian Wu, Yashesh Gaur, Zhuo Chen +8

Large language models (LLMs) have achieved remarkable success in the field of natural language processing, enabling better human-computer interaction using natural language. Howeve…

eess.AS2023

Code-Switching Text Generation and Injection in Mandarin-English ASR

Haibin Yu, Yuxuan Hu, Yao Qian +7

Code-switching speech refers to a means of expression by mixing two or more languages within a single utterance. Automatic Speech Recognition (ASR) with End-to-End (E2E) modeling f…

eess.AS2023★ 2 cited

FoundationTTS: Text-to-Speech for ASR Customization with Generative Language Model

Ruiqing Xue, Yanqing Liu, Lei He +4

Neural text-to-speech (TTS) generally consists of cascaded architecture with separately optimized acoustic model and vocoder, or end-to-end architecture with continuous mel-spectro…

cs.CL2023

Building High-accuracy Multilingual ASR with Gated Language Experts and Curriculum Training

Eric Sun, Jinyu Li, Yuxuan Hu +8

We propose gated language experts and curriculum training to enhance multilingual transformer transducer models without requiring language identification (LID) input from users dur…

cs.CL2022★ 4 cited

Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition

Kenichi Kumatani, Robert Gmyr, Felipe Cruz Salinas +5

The sparsely-gated Mixture of Experts (MoE) can magnify a network capacity with a little computational complexity. In this work, we investigate how multi-lingual Automatic Speech R…

cs.CL2021

FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition

Yichong Leng, Xu Tan, Rui Wang +8

Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence, and can further reduce the word error rate (WER). Although multiple ca…

cs.CL2021★ 30 cited

FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition

Yichong Leng, Xu Tan, Linchen Zhu +7

Error correction techniques have been used to refine the output sentences from automatic speech recognition (ASR) models and achieve a lower word error rate (WER) than original ASR…