Selection of Layers from Self-supervised Learning Models for Predicting Mean-Opinion-Score of Speech
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912534442803200 |
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| author | Liang, Xinyu Cumlin, Fredrik Ungureanu, Victor Reddy, Chandan K. A. Schuldt, Christian Chatterjee, Saikat |
| author_facet | Liang, Xinyu Cumlin, Fredrik Ungureanu, Victor Reddy, Chandan K. A. Schuldt, Christian Chatterjee, Saikat |
| contents | Self-supervised learning (SSL) models like Wav2Vec2, HuBERT, and WavLM have been widely used in speech processing. These transformer-based models consist of multiple layers, each capturing different levels of representation. While prior studies explored their layer-wise representations for efficiency and performance, speech quality assessment (SQA) models predominantly rely on last-layer features, leaving intermediate layers underexamined. In this work, we systematically evaluate different layers of multiple SSL models for predicting mean-opinion-score (MOS). Features from each layer are fed into a lightweight regression network to assess effectiveness. Our experiments consistently show early-layers features outperform or match those from the last layer, leading to significant improvements over conventional approaches and state-of-the-art MOS prediction models. These findings highlight the advantages of early-layer selection, offering enhanced performance and reduced system complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_08962 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Selection of Layers from Self-supervised Learning Models for Predicting Mean-Opinion-Score of Speech Liang, Xinyu Cumlin, Fredrik Ungureanu, Victor Reddy, Chandan K. A. Schuldt, Christian Chatterjee, Saikat Audio and Speech Processing Sound Self-supervised learning (SSL) models like Wav2Vec2, HuBERT, and WavLM have been widely used in speech processing. These transformer-based models consist of multiple layers, each capturing different levels of representation. While prior studies explored their layer-wise representations for efficiency and performance, speech quality assessment (SQA) models predominantly rely on last-layer features, leaving intermediate layers underexamined. In this work, we systematically evaluate different layers of multiple SSL models for predicting mean-opinion-score (MOS). Features from each layer are fed into a lightweight regression network to assess effectiveness. Our experiments consistently show early-layers features outperform or match those from the last layer, leading to significant improvements over conventional approaches and state-of-the-art MOS prediction models. These findings highlight the advantages of early-layer selection, offering enhanced performance and reduced system complexity. |
| title | Selection of Layers from Self-supervised Learning Models for Predicting Mean-Opinion-Score of Speech |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2508.08962 |