Cited by 264Speech Recognition and SynthesisSpeech and Audio ProcessingTopic Modeling

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Layer-Wise Analysis of a Self-Supervised Speech Representation Model

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Pasad, Ankita, Chou, Ju-Chieh, Livescu, Karen·2021·2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

DOI 10.1109/asru51503.2021.9688093

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Abstract

Recently proposed self-supervised learning approaches have been successful for pre-training speech representation models. The utility of these learned representations has been observed empirically, but not much has been studied about the type or extent of information encoded in the pre-trained representations themselves. Developing such insights can help understand the capabilities and limits of these models and enable the research community to more efficiently develop their usage for downstream applications. In this work, we begin to fill this gap by examining one recent and successful pre-trained model (wav2vec 2.0), via its intermediate representation vectors, using a suite of analysis tools. We use the metrics of canonical correlation, mutual information, and performance on simple downstream tasks with non-parametric probes, in order to (i) query for acoustic and linguistic information content, (ii) characterize the evolution of information across model layers, and (iii) understand how fine-tuning the model for automatic speech recognition (ASR) affects these observations. Our findings motivate modifying the fine-tuning protocol for ASR, which produces improved word error rates in a low-resource setting.

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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
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Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020) · arXiv · doi:10.48550/arxiv.2002.05709 · Cited by 10,857
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Oord, A. V. D., Li, Y., & Vinyals, O. (2018) · arXiv · doi:10.48550/arxiv.1807.03748 · Cited by 6,242
“Librispeech: an ASR corpus based on public domain audio books,”
Panayotov, V., Chen, G., Povey, D., & Khudanpur, S. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178964 · Cited by 6,072
RELATIONS BETWEEN TWO SETS OF VARIATES
Hotelling, H. (1936) · Biometrika · doi:10.1093/biomet/28.3-4.321 · Cited by 6,041
Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks
Graves, A., Fernández, S., Gomez, F., & Schmidhuber, J. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143891 · Cited by 5,370
Unsupervised Visual Representation Learning by Context Prediction
Doersch, C., Gupta, A., & Efros, A. A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.167 · Cited by 2,629

Show all 55 references

Research works printed but not linked: 2

10.1109/SLT48900.2021.9383594 · DOI
radford, 2018, Improving language understanding by generative pretraining

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Authors: Pasad, A., Chou, J.-C., & Livescu, K.
Affiliations: Toyota Technological Institute at Chicago (US)
ORCID: Livescu, Karen
Venue: 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)
Publisher: IEEE
Published: 2021-12-13
Pages: 914-921
DOI: 10.1109/asru51503.2021.9688093
OpenAlex: W3179803166
Cited by: 262
References: 55
Citations by year: 2026: 42 · 2025: 65 · 2024: 61 · 2023: 62 · 2022: 29 · 2021: 3
FWCI: 17.90
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: public-domain
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) (publishedVersion)
arXiv (Cornell University) (submittedVersion, public-domain)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Funders: NSF (IIS-1816627)
Air Force Office of Scientific Research (FA9550-18-1-0166)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords: self-supervised speech representation learning, wav2vec 2.0, layer-wise analysis, canonical correlation analysis, mutual information

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Layer-Wise Analysis of a Self-Supervised Speech Representation Model

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