@inproceedings{jain-etal-2024-multi-stage,
title = "Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition",
author = "Jain, Yash and
Chan, David M. and
Dheram, Pranav and
Khare, Aparna and
Shonibare, Olabanji and
Ravichandran, Venkatesh and
Ghosh, Shalini",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1045",
pages = "11969--11980",
abstract = "Recent advances in machine learning have demonstrated that multi-modal pre-training can improve automatic speech recognition (ASR) performance compared to randomly initialized models, even when models are fine-tuned on uni-modal tasks. Existing multi-modal pre-training methods for the ASR task have primarily focused on single-stage pre-training where a single unsupervised task is used for pre-training followed by fine-tuning on the downstream task. In this work, we introduce a novel method combining multi-modal and multi-task unsupervised pre-training with a translation-based supervised mid-training approach. We empirically demonstrate that such a multi-stage approach leads to relative word error rate (WER) improvements of up to 38.45{\%} over baselines on both Librispeech and SUPERB. Additionally, we share several important findings for choosing pre-training methods and datasets.",
}
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<abstract>Recent advances in machine learning have demonstrated that multi-modal pre-training can improve automatic speech recognition (ASR) performance compared to randomly initialized models, even when models are fine-tuned on uni-modal tasks. Existing multi-modal pre-training methods for the ASR task have primarily focused on single-stage pre-training where a single unsupervised task is used for pre-training followed by fine-tuning on the downstream task. In this work, we introduce a novel method combining multi-modal and multi-task unsupervised pre-training with a translation-based supervised mid-training approach. We empirically demonstrate that such a multi-stage approach leads to relative word error rate (WER) improvements of up to 38.45% over baselines on both Librispeech and SUPERB. Additionally, we share several important findings for choosing pre-training methods and datasets.</abstract>
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%0 Conference Proceedings
%T Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition
%A Jain, Yash
%A Chan, David M.
%A Dheram, Pranav
%A Khare, Aparna
%A Shonibare, Olabanji
%A Ravichandran, Venkatesh
%A Ghosh, Shalini
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F jain-etal-2024-multi-stage
%X Recent advances in machine learning have demonstrated that multi-modal pre-training can improve automatic speech recognition (ASR) performance compared to randomly initialized models, even when models are fine-tuned on uni-modal tasks. Existing multi-modal pre-training methods for the ASR task have primarily focused on single-stage pre-training where a single unsupervised task is used for pre-training followed by fine-tuning on the downstream task. In this work, we introduce a novel method combining multi-modal and multi-task unsupervised pre-training with a translation-based supervised mid-training approach. We empirically demonstrate that such a multi-stage approach leads to relative word error rate (WER) improvements of up to 38.45% over baselines on both Librispeech and SUPERB. Additionally, we share several important findings for choosing pre-training methods and datasets.
%U https://aclanthology.org/2024.lrec-main.1045
%P 11969-11980
Markdown (Informal)
[Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition](https://aclanthology.org/2024.lrec-main.1045) (Jain et al., LREC-COLING 2024)
ACL
- Yash Jain, David M. Chan, Pranav Dheram, Aparna Khare, Olabanji Shonibare, Venkatesh Ravichandran, and Shalini Ghosh. 2024. Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 11969–11980, Torino, Italia. ELRA and ICCL.