31 citations · 53 across the 5 of their papers we have counts for
5 papers
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition
Ye Bai, Jingping Chen, Jitong Chen +52
Modern automatic speech recognition (ASR) model is required to accurately transcribe diverse speech signals (from different domains, languages, accents, etc) given the specific con…
Language Adaptive Cross-lingual Speech Representation Learning with Sparse Sharing Sub-networks
Yizhou Lu, Mingkun Huang, Xinghua Qu +2
Unsupervised cross-lingual speech representation learning (XLSR) has recently shown promising results in speech recognition by leveraging vast amounts of unlabeled data across mult…
The Accented English Speech Recognition Challenge 2020: Open Datasets, Tracks, Baselines, Results and Methods
Xian Shi, Fan Yu, Yizhou Lu +5
The variety of accents has posed a big challenge to speech recognition. The Accented English Speech Recognition Challenge (AESRC2020) is designed for providing a common testbed and…
Data Augmentation for End-to-end Code-switching Speech Recognition
Chenpeng Du, Hao Li, Yizhou Lu +2
Training a code-switching end-to-end automatic speech recognition (ASR) model normally requires a large amount of data, while code-switching data is often limited. In this paper, t…
Modular End-to-end Automatic Speech Recognition Framework for Acoustic-to-word Model
Qi Liu, Zhehuai Chen, Hao Li +3
End-to-end (E2E) systems have played a more and more important role in automatic speech recognition (ASR) and achieved great performance. However, E2E systems recognize output word…