探究说话人预训练模型的合理有效性及其协同能力在 SingMOS 预测中的应用
Investigating the Reasonable Effectiveness of Speaker Pre-Trained Models and their Synergistic Power for SingMOS Prediction
AI总结：
本研究验证了说话人识别预训练模型对 SingMOS 预测的有效性，并提出基于 Bhattacharya 距离的 BATCH 融合框架，通过融合 SPTMs 取得了最优性能。
摘要：
本研究聚焦于歌声平均意见得分（SingMOS）预测。先前研究已表明使用最先进的（SOTA）预训练模型（PTMs）能带来性能提升。然而，这些研究尚未探索如 x-vector、ECAPA 等说话人识别语音预训练模型（SPTMs），我们假设它们对 SingMOS 预测最为有效。我们认为，由于其说话人识别预训练，它们能够比其他 PTMs 更好地从合成歌声中捕捉细粒度声乐特征（如音高、音调、强度）。我们使用包括 SPTMs 和音乐 PTMs 在内的 SOTA PTMs 进行的实验验证了这一假设。此外，我们引入了一种新颖的融合框架 BATCH，该框架使用 Bhattacharya 距离进行 PTMs 融合。通过 BATCH 融合说话人识别 SPTMs，我们报告了与所有单个 PTMs 和基线融合技术相比的最高性能，并达到了 SOTA。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
