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Layer-Wise Analysis of a Self-Supervised Speech Representation Model
Open accessAbstract
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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“Layer-wise analyses of self-supervised speech models showthat acoustic and speaker-related cues concentrate in lower and intermediate layers while deeper layers drift toward lexical and semantic content [19, 20], and that a learnable combination of all layers consistently outperforms the last-layer representation across diverse speech tasks [21].”
From MOSS-Audio Technical Report (2026) · cites this paper as [19]
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Metadata
- 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
- 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
- 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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