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In this work, we aim at identifying the speaker in interactions between mothers and children with Down syndrome (DS) using audio. We collected audio from a session in which children with DS solved puzzles, and their mothers were by their side. We generated a dataset by manually annotating human speech activity and non-speech. We used machine learning to perform four experiments, including individual and generalized models achieving on average F1-scores of 0.74 (five-class model) and 0.84 (two-class) for individual models and 0.69 (five-class) and 0.82 (two-class) for the generalized models. Our results can be helpful to behavioral researchers, therapists, and those interested in better understanding how mother–child interactions unfold in naturalistic settings.

Speaker Identification in Interactions between Mothers and Children with Down Syndrome via Audio Analysis: A Case Study in Mexico