The Mice Autism Detection via Ultrasound Vocalization (MAD-UV) Challenge introduces the first INTERSPEECH challenge focused on detecting autism spectrum disorder (ASD) in mice through their vocalizations. Participants are tasked with developing models to automatically classify mice as either wild-type or ASD models based on recordings with a high sampling rate. Our baseline system employs a simple CNN-based classification using three different spectrogram features. Results demonstrate the feasibility of automated ASD detection, with the considered audible-range features achieving the best performance (UAR of 0.600 for segment-level and 0.625 for subject-level classification). This challenge bridges speech technology and biomedical research, offering opportunities to advance our understanding of ASD models through machine learning approaches. The findings suggest promising directions for vocalization analysis and highlight the potential value of audible and ultrasound vocalizations in ASD detection.

翻译：基于超声发声的小鼠自闭症检测（MAD-UV）挑战赛推出了首个聚焦于通过小鼠发声检测自闭症谱系障碍（ASD）的INTERSPEECH挑战赛。参赛者的任务是开发模型，基于高采样率的录音，自动将小鼠分类为野生型或ASD模型。我们的基线系统采用简单的基于CNN的分类方法，使用了三种不同的声谱图特征。结果证明了自动化ASD检测的可行性，其中所考虑的可听范围特征取得了最佳性能（片段级分类的未加权平均召回率为0.600，个体级分类为0.625）。本挑战赛搭建了语音技术与生物医学研究之间的桥梁，为通过机器学习方法推进我们对ASD模型的理解提供了机会。这些发现为发声分析指出了有前景的方向，并突显了可听声与超声发声在ASD检测中的潜在价值。