Sensitive Quantification of Cerebellar Speech Abnormalities Using Deep Learning Models

Abstract

Objective: Objective, sensitive, and meaningful disease assessments are critical to support clinical trials and clinical care. Speech changes are one of the earliest and most evident manifestations of cerebellar ataxias. This work aims to develop models that can accurately identify and quantify clinical signs of ataxic speech. Methods: We use convolutional neural networks to capture the motor speech phenotype of cerebellar ataxia based on time and frequency partial derivatives of log-mel spectrogram representations of speech. We train classification models to distinguish patients with ataxia from healthy controls as well as regression models to estimate disease severity. Results: Classification models were able to accurately distinguish healthy controls from individuals with ataxia, including ataxia participants who clinicians rated as having no detectable clinical deficits in speech. Regression models produced accurate estimates of disease severity, were able to measure subclinical signs of ataxia, and captured disease progression over time. Conclusion: Convolutional networks trained on time and frequency partial derivatives of the speech signal can detect sub-clinical speech changes in ataxias and sensitively measure disease change over time. Significance: Learned speech analysis models have the potential to aid early detection of disease signs in ataxias and provide sensitive, low-burden assessment tools in support of clinical trials and neurological care.