Sandra Ottl
Papers
1
Total Citations
16
H-Index
1
About
Sandra Ottl’s research lies at the intersection of machine learning, speech processing, and neurodevelopmental conditions, with a particular focus on autism spectrum conditions (ASC). Her most cited work, “Recognition of Echolalic Autistic Child Vocalisations Utilising Convolutional Recurrent Neural Networks” (2018, 16 citations), introduces a novel dataset of 15 echolalic vocalisations and demonstrates how deep learning architectures can be trained to recognise these atypical speech patterns. This contribution is significant because echolalia—the repetition of others’ speech—is a common but understudied communicative behaviour in autistic children, and Ottl’s approach offers a pathway toward more inclusive speech recognition systems. By combining convolutional and recurrent neural networks, she addresses the challenge of modelling temporal and spectral features in non-standard vocalisations. Her work has implications for assistive technology and clinical assessment, providing a data-driven foundation for understanding and supporting autistic communication. Ottl’s research exemplifies how computational methods can be applied to real-world, human-centred problems, bridging gaps between artificial intelligence and neurodiversity.
Research Focus
Key Achievements
Top Papers
- 1