Towards Interpreting Deep Learning Models to Understand Loss of Speech Intelligibility in Speech Disorders - Step 1: CNN Model-Based Phone Classification

Perceptual measurement is still the most common method for assessing disordered speech in clinical practice. The subjectivity of such a measure, strongly due to human nature, but also to its lack of interpretation with regard to local alterations in speech units, strongly motivates a sophisticated tool for objective evaluation. Of interest is the increasing performance of Deep Neural Networks in speech applications, but more importantly the fact that they are no longer considered as black boxes. The work carried out here is the first step in a long-term research project, which aims to determine the linguistic units that contribute most to the maintenance or loss of the intelligibility in speech disorders. In this context, we study a CNN trained on normal speech for a classification task of phones and tested on pathological speech. The aim of this first study is to analyze the response of the CNN model to disordered speech in order to study later its effectiveness in providing relevant knowledge in terms of speech severity or loss of intelligibility. Compared to perceptual severity and intelligibility measures, the results revealed a very strong correlation between these metrics and our classifier performance scores, which is very promising for future work.