2025/06/02 by Tahiya Chowdhury, Chowdhury, Tahiya, Verónica Romero +1
Computer Science · #Applications (stat.AP) #Audio and Speech Processing (eess.AS) #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #FOS: Electrical engineering #I.2 #J.4 #K.4 #Sound (cs.SD) #Speech Recognition and Synthesis #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2506.11072
openalex publication_date 2025/06/02 · openalex created_date 2025/10/11 · openalex updated_date 2026/07/28
Machine learning-based behavioral models rely on features extracted from audio-visual recordings. The recordings are processed using open-source tools to extract speech features for classification models. These tools often lack validation to ensure reliability in capturing behaviorally relevant information. This gap raises concerns about reproducibility and fairness across diverse populations and contexts. Speech processing tools, when used outside of their design context, can fail to capture behavioral variations equitably and can then contribute to bias. We evaluate speech features extracted from two widely used speech analysis tools, OpenSMILE and Praat, to assess their reliability when considering adolescents with autism. We observed considerable variation in features across tools, which influenced model performance across context and demographic groups. We encourage domain-relevant verification to enhance the reliability of machine learning models in clinical applications.