Automatic Children Speech Sound Disorder Detection with Age and Speaker Bias Mitigation

Addressing speech sound disorders (SSD) in early childhood is pivotal for mitigating cognitive and communicative impediments. Previous works on automatic SSD detection rely on audio features without considering the age and speaker bias which results in degraded performance. In this paper, we propose an SSD detection system in which debiasing techniques are applied to mitigate the biases. For the age bias, we use a multi-head model where the feature extractor is shared across different age groups but the final decision is made using the age-dependent classifier. For the speaker bias, we augment the dataset by mixing the audios of the multiple speakers in the same age group. When evaluated with our Korean SSD dataset, the proposed method showed significant improvements over previous approaches.