@inproceedings{fu-etal-2024-wav2vec,
title = "wav2vec-{S}: Adapting Pre-trained Speech Models for Streaming",
author = "Fu, Biao and
Fan, Kai and
Liao, Minpeng and
Chen, Yidong and
Shi, Xiaodong and
Huang, Zhongqiang",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.681",
pages = "11465--11480",
abstract = "Pre-trained speech models, such as wav2vec 2.0, have significantly advanced speech-related tasks, including speech recognition and translation. However, their applicability in streaming scenarios is limited because these models are trained on complete utterances, leading to a mismatch with incremental streaming inputs. This paper identifies three critical design aspects within the architecture of wav2vec 2.0 and proposes a novel model, wav2vec-S, which incorporates simple modifications to ensure consistent speech representations during both training and inference phases for streaming speech inputs. Furthermore, we demonstrate that wav2vec-S models can be efficiently adapted from pre-trained wav2vec 2.0 models through continued pre-training and effectively finetuned to meet various latency requirements in downstream applications. Experiments on speech recognition and translation tasks show that wav2vec-S outperforms strong baseline models and achieves a superior balance between quality and latency.",
}
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<abstract>Pre-trained speech models, such as wav2vec 2.0, have significantly advanced speech-related tasks, including speech recognition and translation. However, their applicability in streaming scenarios is limited because these models are trained on complete utterances, leading to a mismatch with incremental streaming inputs. This paper identifies three critical design aspects within the architecture of wav2vec 2.0 and proposes a novel model, wav2vec-S, which incorporates simple modifications to ensure consistent speech representations during both training and inference phases for streaming speech inputs. Furthermore, we demonstrate that wav2vec-S models can be efficiently adapted from pre-trained wav2vec 2.0 models through continued pre-training and effectively finetuned to meet various latency requirements in downstream applications. Experiments on speech recognition and translation tasks show that wav2vec-S outperforms strong baseline models and achieves a superior balance between quality and latency.</abstract>
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%0 Conference Proceedings
%T wav2vec-S: Adapting Pre-trained Speech Models for Streaming
%A Fu, Biao
%A Fan, Kai
%A Liao, Minpeng
%A Chen, Yidong
%A Shi, Xiaodong
%A Huang, Zhongqiang
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F fu-etal-2024-wav2vec
%X Pre-trained speech models, such as wav2vec 2.0, have significantly advanced speech-related tasks, including speech recognition and translation. However, their applicability in streaming scenarios is limited because these models are trained on complete utterances, leading to a mismatch with incremental streaming inputs. This paper identifies three critical design aspects within the architecture of wav2vec 2.0 and proposes a novel model, wav2vec-S, which incorporates simple modifications to ensure consistent speech representations during both training and inference phases for streaming speech inputs. Furthermore, we demonstrate that wav2vec-S models can be efficiently adapted from pre-trained wav2vec 2.0 models through continued pre-training and effectively finetuned to meet various latency requirements in downstream applications. Experiments on speech recognition and translation tasks show that wav2vec-S outperforms strong baseline models and achieves a superior balance between quality and latency.
%U https://aclanthology.org/2024.findings-acl.681
%P 11465-11480
Markdown (Informal)
[wav2vec-S: Adapting Pre-trained Speech Models for Streaming](https://aclanthology.org/2024.findings-acl.681) (Fu et al., Findings 2024)
ACL
- Biao Fu, Kai Fan, Minpeng Liao, Yidong Chen, Xiaodong Shi, and Zhongqiang Huang. 2024. wav2vec-S: Adapting Pre-trained Speech Models for Streaming. In Findings of the Association for Computational Linguistics ACL 2024, pages 11465–11480, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.