MADUV: The 1st INTERSPEECH Mice Autism Detection via Ultrasound Vocalization Challenge
This work addresses the problem of detecting autism in mice for biomedical researchers, but it is incremental as it introduces a new benchmark challenge.
The MADUV challenge tackled automated detection of autism spectrum disorder in mice using ultrasound vocalizations, achieving a baseline performance of 0.625 UAR at the subject-level classification.
The Mice Autism Detection via Ultrasound Vocalization (MADUV) 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.