Improving Automatic Speech Recognition for Dutch Children with Developmental Language Disorder
Traditional Digital Signal Processing Techniques for Data Augmentation
S.E. Gilbert (TU Delft - Electrical Engineering, Mathematics and Computer Science)
O.E. Scharenborg – Graduation committee member (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Y. Zhang – Mentor (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
This thesis examines whether Dutch adult dysarthric speech can be transformed into speech that is sufficiently child-like to improve Automatic Speech Recognition (ASR) for Dutch children with Developmental Language Disorder (DLD). Due to the limited availability of annotated speech data from children with DLD, existing ASR systems often struggle to accurately recognize this population. To address this data scarcity issue, this work explores the use of traditional Digital Signal Processing (DSP) techniques to augment recordings of Dutch adult speakers with dysarthria to sound more child-like, while preserving overlapping characteristics with DLD.
A series of transformation methods targeting pitch, vocal tract characteristics, spectral properties, and prosodic patterns are implemented and combined into multiple transformation pipelines. The resulting synthetic speech is evaluated through ASR fine-tuning experiments using Whisper-large-v3 and Word Error Rate (WER), as well as an objective speech-quality assessment using Automatic Mean Opinion Score (AMOS) prediction.
The results show that the synthetic child-like speech can indeed improve ASR performance when used as augmentation data. The best-performing configuration combines a targeted subset of mild and moderate hypokinetic and ataxic dysarthric speakers with a prosody-enhanced transformation pipeline, and achieved a WER of 29.00\%, outperforming both the zero-shot Whisper-large-v3 baseline (46.48\%) and a CHILDES-only fine-tuning baseline (31.57\%).
The findings further indicate that source-speaker similarity is more important than dataset size and that conservative prosodic modifications contribute meaningfully to the usefulness of synthetic speech data for ASR fine-tuning. Overall, the results demonstrate that carefully selected dysarthric speech combined with interpretable DSP transformations can provide valuable augmentation data for Dutch children with DLD.