Speech foundation models, trained on vast datasets, have opened unique opportunities in addressing challenging low-resource speech understanding, such as child speech. In this work, we explore the capabilities of speech foundation models on child-adult speaker diarization. We show that exemplary foundation models can achieve 39.5% and 62.3% relative reductions in Diarization Error Rate and Speaker Confusion Rate, respectively, compared to previous speaker diarization methods. In addition, we benchmark and evaluate the speaker diarization results of the speech foundation models with varying the input audio window size, speaker demographics, and training data ratio. Our results highlight promising pathways for understanding and adopting speech foundation models to facilitate child speech understanding.

翻译：语音基础模型通过海量数据集训练，为应对低资源语音理解（如儿童语音）等挑战性任务开辟了独特路径。本研究探索了语音基础模型在儿童-成人说话人日志任务中的性能表现。实验表明，相较于传统说话人日志方法，典型基础模型能分别实现39.5%的说话人日志错误率相对降低与62.3%的说话人混淆率相对降低。此外，我们通过系统化基准测试，评估了输入音频窗口长度、说话人人口统计学特征及训练数据比例等因素对语音基础模型说话人日志结果的影响。本研究结果为理解并采用语音基础模型以促进儿童语音理解提供了可行路径。