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Automatic Detection of Direct and Self-Repetitions in Naturalistic Speech Recordings of French- and Dutch-Speaking Autistic Children

This study investigates the use of cosine similarity measures across syntactic, lexical, and semantic vector repre- sentations to detect repetitions in the spontaneous speech of autistic children. It focuses on direct repetitions (i.e., immediate verbatim repetitions of linguistic output produced by another individual) and self-repetitions (i.e., within-speaker recurrence). The performance of similarity-based methods is then compared with state-of-the-art black-box classification models based on BERT, trained on the same data. Using spontaneous speech data from French- and Dutch- speaking autistic children, the results show that lexical and semantic similarity provide reliable cues for identifying self-repetitions, achieving high precision and recall, with F1-scores exceeding 83%, comparable to those obtained by BERT-based models. In contrast, direct repetitions are more difficult to detect using similarity-based approaches, with BERT models clearly outperforming them and reaching F1-scores above 73%. Across all conditions, syntactic similarity consistently underperforms relative to lexical and semantic measures. These findings highlight the strengths and limitations of similarity-based approaches and suggest directions for future research, particularly in improving the detection of direct repetitions and assessing the cross-linguistic generalizability of these methods.