Designing a speech quality assessment (SQA) system for estimating mean-opinion-score (MOS) of multi-rate speech with varying sampling frequency (16-48 kHz) is a challenging task. The challenge arises due to the limited availability of a MOS-labeled training dataset comprising multi-rate speech samples. While self-supervised learning (SSL) models have been widely adopted in SQA to boost performance, a key limitation is that they are pretrained on 16 kHz speech and therefore discard high-frequency information present in higher sampling rates. To address this issue, we propose a spectrogram-augmented SSL method that incorporates high-frequency features (up to 48 kHz sampling rate) through a parallel-branch architecture. We further introduce a two-step training scheme: the model is first pre-trained on a large 48 kHz dataset and then fine-tuned on a smaller multi-rate dataset. Experimental results show that leveraging high-frequency information overlooked by SSL features is crucial for accurate multi-rate SQA, and that the proposed two-step training substantially improves generalization when multi-rate data is limited.

翻译：设计一个能够评估多速率语音（采样频率范围为16-48 kHz）平均意见得分（MOS）的语音质量评估（SQA）系统是一项具有挑战性的任务。这一挑战主要源于包含多速率语音样本且带有MOS标注的训练数据集十分有限。尽管自监督学习（SSL）模型在SQA领域已被广泛采用以提升性能，但其一个关键局限在于，这些模型通常在16 kHz语音上进行预训练，因此会丢弃更高采样率（如48 kHz）语音中存在的高频信息。为解决此问题，我们提出了一种谱图增强的SSL方法，该方法通过一种并行分支架构来融合高频特征（最高支持48 kHz采样率）。我们进一步引入了一种两阶段训练方案：模型首先在一个大规模的48 kHz数据集上进行预训练，然后在一个较小的多速率数据集上进行微调。实验结果表明，利用被SSL特征所忽略的高频信息对于准确的多速率SQA至关重要，并且所提出的两阶段训练方案在多速率数据有限的情况下，显著提升了模型的泛化能力。