Automatic Assessment of Sentence-Level Dysarthria Intelligibility Using BLSTM

Abstract

Dysarthria is a motor speech impairment, often characterized by slow and slurred speech that is generally incomprehensible by human listeners. An understanding of the intelligibility level of the patient's dysarthric speech can provide an insight into the progression/status of the underlying cause and is essential for planning therapy. Automatic assessment of dysarthric speech intelligibility can be of immense value and serve to assist speech language pathologists in diagnosis and therapy. However, this is a non-trivial problem due to the high intra and inter speaker variability in dysarthric speech. In this article we propose a machine learning-based method to automatically classify dysarthric speech into intelligible (I) and non-intelligible (NI) using Bidirectional Long-Short Term Memory (BLSTM) Networks. We explored balancing of training data to represent both the classes almost equally and its implications on the binary classification. Additionally, we present a mechanism to use the available pre-trained acoustic models for transfer-learning. It was observed that the transfer learning method was able to handle channel noise. This technique provided significant improvement of roughly 6% as compared to traditional machine learning method.