Investigating the Reasonable Effectiveness of Speaker Pre-Trained Models and their Synergistic Power for SingMOS Prediction
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| author | Phukan, Orchid Chetia Girish Akhtar, Mohd Mujtaba Behera, Swarup Ranjan Reddy, Pailla Balakrishna Buduru, Arun Balaji Sharma, Rajesh |
| author_facet | Phukan, Orchid Chetia Girish Akhtar, Mohd Mujtaba Behera, Swarup Ranjan Reddy, Pailla Balakrishna Buduru, Arun Balaji Sharma, Rajesh |
| contents | In this study, we focus on Singing Voice Mean Opinion Score (SingMOS) prediction. Previous research have shown the performance benefit with the use of state-of-the-art (SOTA) pre-trained models (PTMs). However, they haven't explored speaker recognition speech PTMs (SPTMs) such as x-vector, ECAPA and we hypothesize that it will be the most effective for SingMOS prediction. We believe that due to their speaker recognition pre-training, it equips them to capture fine-grained vocal features (e.g., pitch, tone, intensity) from synthesized singing voices in a much more better way than other PTMs. Our experiments with SOTA PTMs including SPTMs and music PTMs validates the hypothesis. Additionally, we introduce a novel fusion framework, BATCH that uses Bhattacharya Distance for fusion of PTMs. Through BATCH with the fusion of speaker recognition SPTMs, we report the topmost performance comparison to all the individual PTMs and baseline fusion techniques as well as setting SOTA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02232 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Investigating the Reasonable Effectiveness of Speaker Pre-Trained Models and their Synergistic Power for SingMOS Prediction Phukan, Orchid Chetia Girish Akhtar, Mohd Mujtaba Behera, Swarup Ranjan Reddy, Pailla Balakrishna Buduru, Arun Balaji Sharma, Rajesh Audio and Speech Processing Sound In this study, we focus on Singing Voice Mean Opinion Score (SingMOS) prediction. Previous research have shown the performance benefit with the use of state-of-the-art (SOTA) pre-trained models (PTMs). However, they haven't explored speaker recognition speech PTMs (SPTMs) such as x-vector, ECAPA and we hypothesize that it will be the most effective for SingMOS prediction. We believe that due to their speaker recognition pre-training, it equips them to capture fine-grained vocal features (e.g., pitch, tone, intensity) from synthesized singing voices in a much more better way than other PTMs. Our experiments with SOTA PTMs including SPTMs and music PTMs validates the hypothesis. Additionally, we introduce a novel fusion framework, BATCH that uses Bhattacharya Distance for fusion of PTMs. Through BATCH with the fusion of speaker recognition SPTMs, we report the topmost performance comparison to all the individual PTMs and baseline fusion techniques as well as setting SOTA. |
| title | Investigating the Reasonable Effectiveness of Speaker Pre-Trained Models and their Synergistic Power for SingMOS Prediction |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2506.02232 |