标题： 利用大语言模型进行可扩展的非侵入式语音质量评估 显示英文标题

标题： Leveraging LLMs for Scalable Non-intrusive Speech Quality Assessment

摘要： 非侵入式语音质量评估（SQA）系统面临训练数据有限和人工标注成本高昂的问题，这阻碍了它们在实时会议通话中的泛化能力。 在本工作中，我们提出利用大型语言模型（LLMs）作为伪评分者来评估语音质量，以解决这些数据瓶颈问题。 我们构建了LibriAugmented数据集，该数据集包含101,129个带有模拟退化的语音片段，由微调的听觉LLM（Vicuna-7b-v1.5）进行标记。 我们比较了三种训练策略：使用人工标记的数据、使用LLM标记的数据，以及两阶段方法（先在LLM标记上预训练，然后在人工标记上微调），同时使用DNSMOS Pro和DeePMOS。 我们在多种跨语言和质量退化情况的数据集上进行了测试。 虽然与人工标记训练相比，LLM标记训练的结果好坏参半，但我们提供了实证证据表明，两阶段方法提高了泛化性能（例如，在NISQA_TEST_LIVETALK上DNSMOS Pro的PCC达到0.63 vs. 0.55，以及在腾讯带有混响的测试中达到0.73 vs. 0.65 PCC）。 我们的研究结果展示了将LLMs用作可扩展伪评分者的潜力，为数据限制问题提供了一种成本效益高的解决方案。 显示英文摘要

摘要： Non-intrusive speech quality assessment (SQA) systems suffer from limited training data and costly human annotations, hindering their generalization to real-time conferencing calls. In this work, we propose leveraging large language models (LLMs) as pseudo-raters for speech quality to address these data bottlenecks. We construct LibriAugmented, a dataset consisting of 101,129 speech clips with simulated degradations labeled by a fine-tuned auditory LLM (Vicuna-7b-v1.5). We compare three training strategies: using human-labeled data, using LLM-labeled data, and a two-stage approach (pretraining on LLM labels, then fine-tuning on human labels), using both DNSMOS Pro and DeePMOS. We test on several datasets across languages and quality degradations. While LLM-labeled training yields mixed results compared to human-labeled training, we provide empirical evidence that the two-stage approach improves the generalization performance (e.g., DNSMOS Pro achieves 0.63 vs. 0.55 PCC on NISQA_TEST_LIVETALK and 0.73 vs. 0.65 PCC on Tencent with reverb). Our findings demonstrate the potential of using LLMs as scalable pseudo-raters for speech quality assessment, offering a cost-effective solution to the data limitation problem.