无听力图：利用已有评分进行个性化语音可懂度预测
No Audiogram: Leveraging Existing Scores for Personalized Speech Intelligibility Prediction
AI总结：
本文提出SSIPNet，利用听者已有的少量（音频，评分）对构建高维能力表示，在不依赖听力图的情况下实现更准确的个性化语音可懂度预测。
摘要：
个性化语音可懂度预测具有挑战性。以往方法主要依赖听力图，但听力图本质上精度有限，因为它仅捕捉听者对纯音的听阈。我们提出一种新方法，不引入额外的听者特征，而是利用个体已有的可懂度数据来预测其在新音频上的表现。我们提出了支持样本驱动的可懂度预测网络（SSIPNet），这是一个深度学习模型，利用语音基础模型从多个支持（音频，评分）对中构建听者语音识别能力的高维表示，从而实现对未见音频的准确预测。在Clarity Prediction Challenge数据集上的结果表明，即使仅使用少量支持（音频，评分）对，我们的方法也优于基于听力图的预测。我们的工作为个性化语音可懂度预测提供了一种新范式。
英文摘要：
Personalized speech intelligibility prediction is challenging. Previous approaches have mainly relied on audiograms, which are inherently limited in accuracy as they only capture a listener's hearing threshold for pure tones. Rather than incorporating additional listener features, we propose a novel approach that leverages an individual's existing intelligibility data to predict their performance on new audio. We introduce the Support Sample-Based Intelligibility Prediction Network (SSIPNet), a deep learning model that leverages speech foundation models to build a high-dimensional representation of a listener's speech recognition ability from multiple support (audio, score) pairs, enabling accurate predictions for unseen audio. Results on the Clarity Prediction Challenge dataset show that, even with a small number of support (audio, score) pairs, our method outperforms audiogram-based predictions. Our work presents a new paradigm for personalized speech intelligibility prediction.
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。
