Weakly Supervised Phonological Features for Pathological Speech Analysis

Paralinguistic properties of speech are essential in analyzing and choosing optimal treatment options for patients with speech disorders. However, automatic modeling of these characteristics is difficult due to the lack of labeled speech datasets describing paralinguistic properties, especially at the frame-level. In this paper, we propose a weakly supervised training method which exploits the known acoustic properties of phonemes by training an ASR model with an interpretable frame-level phonological feature bottleneck layer. Subsequently, we assess the viability of these phonological features in speech pathology analysis by developing corresponding models for intelligibility prediction and speech pathology classification. Models using our proposed phonological features perform similar to other state-of-the-art acoustic features on both tasks with a classification accuracy of 75% and a 8.43 RMSE on speech intelligibility prediction. In contrast to others, our phonological features are text-independent and highly interpretable, providing potentially useful insights for speech therapists.