Automatic vocalisation-based detection of fragile X syndrome and Rett syndrome
Florian B Pokorny1,2,3, Maximilian Schmitt4, Mathias Egger5
1iDN - interdisciplinary Developmental Neuroscience, Division of Phoniatrics, Medical University of Graz, Graz, Austria. florian.pokorny@medunigraz.at.
Insights
Early detection of Fragile X syndrome (FXS) and Rett syndrome (RTT) may be possible through infant vocalization analysis. Acoustic features can help identify these developmental disorders sooner, enabling timely intervention.
Area of Science:
- Developmental Neuroscience
- Speech-Language Pathology
- Computational Linguistics
Background:
- Fragile X syndrome (FXS) and Rett syndrome (RTT) are typically diagnosed in toddlerhood.
- Speech-language deficits are key symptoms, but early acoustic vocalization patterns remain understudied.
- Earlier identification can significantly improve intervention and family support.
Purpose of the Study:
- To investigate the feasibility of using intelligent audio analysis for early identification of FXS and RTT.
- To analyze acoustic features of infant vocalizations in relation to FXS and RTT.
- To develop computational methods for distinguishing affected infants from typically developing peers.
Main Methods:
- Collected 4454 home-recorded vocalizations from infants aged 6-11 months (3 FXS, 3 RTT, 6 typically developing controls).
- Extracted 88 standardized acoustic features from vocalizations.
- Trained linear kernel support vector machines for classification tasks (FXS vs. TD, RTT vs. TD, atypical vs. TD, FXS vs. RTT vs. TD).
Main Results:
- Perfect classification accuracy was achieved for FXS vs. TD, RTT vs. TD, and atypical development vs. TD.
- Classification accuracy for FXS vs. RTT vs. TD was 75% (9 out of 12 infants).
- Spectral/cepstral and energy-related acoustic features were most crucial for accurate classification.
Conclusions:
- Infant vocalization acoustics show distinct patterns in FXS and RTT.
- Intelligent audio analysis holds promise for earlier diagnosis of these developmental disorders.
- This research provides a foundation for developing automated tools for early detection and intervention.
Abstract:
Fragile X syndrome (FXS) and Rett syndrome (RTT) are developmental disorders currently not diagnosed before toddlerhood. Even though speech-language deficits are among the key symptoms of both conditions, little is known about infant vocalisation acoustics for an automatic earlier identification of affected individuals. To bridge this gap, we applied intelligent audio analysis methodology to a compact dataset of 4454 home-recorded vocalisations of 3 individuals with FXS and 3 individuals with RTT aged 6 to 11 months, as well as 6 age- and gender-matched typically developing controls (TD). On the basis of a standardised set of 88 acoustic features, we trained linear kernel support vector machines to evaluate the feasibility of automatic classification of (a) FXS vs TD, (b) RTT vs TD, (c) atypical development (FXS+RTT) vs TD, and (d) FXS vs RTT vs TD. In paradigms (a)-(c), all infants were correctly classified; in paradigm (d), 9 of 12 were so. Spectral/cepstral and energy-related features were most relevant for classification across all paradigms. Despite the small sample size, this study reveals new insights into early vocalisation characteristics in FXS and RTT, and provides technical underpinnings for a future earlier identification of affected individuals, enabling earlier intervention and family counselling.
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