EH-MAM: Easy-to-Hard Masked Acoustic Modeling for Self-Supervised Speech Representation Learning
Ashish Seth, Ramaneswaran Selvakumar, S Sakshi, Sonal Kumar, Sreyan Ghosh, Dinesh Manocha
Abstract
In this paper, we present EH-MAM (Easy-to-Hard adaptive Masked Acoustic Modeling), a novel self-supervised learning approach for speech representation learning. In contrast to the prior methods that use random masking schemes for Masked Acoustic Modeling (MAM), we introduce a novel selective and adaptive masking strategy. Specifically, during SSL training, we progressively introduce harder regions to the model for reconstruction. Our approach automatically selects hard regions and is built on the observation that the reconstruction loss of individual frames in MAM can provide natural signals to judge the difficulty of solving the MAM pre-text task for that frame. To identify these hard regions, we employ a teacher model that first predicts the frame-wise losses and then decides which frames to mask. By learning to create challenging problems, such as identifying harder frames and solving them simultaneously, the model is able to learn more effective representations and thereby acquire a more comprehensive understanding of the speech. Quantitatively, EH-MAM outperforms several state-of-the-art baselines across various low-resource speech recognition and SUPERB benchmarks by 5%-10%. Additionally, we conduct a thorough analysis to show that the regions masked by EH-MAM effectively capture useful context across speech frames.- Anthology ID:
- 2024.emnlp-main.366
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6386–6400
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.emnlp-main.366/
- DOI:
- 10.18653/v1/2024.emnlp-main.366
- Cite (ACL):
- Ashish Seth, Ramaneswaran Selvakumar, S Sakshi, Sonal Kumar, Sreyan Ghosh, and Dinesh Manocha. 2024. EH-MAM: Easy-to-Hard Masked Acoustic Modeling for Self-Supervised Speech Representation Learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 6386–6400, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- EH-MAM: Easy-to-Hard Masked Acoustic Modeling for Self-Supervised Speech Representation Learning (Seth et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.emnlp-main.366.pdf