A Multi-Smartwatch System for Assessing Speech Characteristics of People with Dysarthria in Group Settings
This technology assists speech-language pathologists in monitoring treatment progress for patients with speech disorders outside clinical settings, though it appears incremental as it builds on existing methods for speech signal processing.
The paper tackled the problem of analyzing speech characteristics for people with dysarthria in group settings by developing a multi-smartwatch system that captures and separates mixed speech signals, computing metrics like loudness and pitch, with validation on data from Parkinson's disease patients and healthy controls.
Speech-language pathologists (SLPs) frequently use vocal exercises in the treatment of patients with speech disorders. Patients receive treatment in a clinical setting and need to practice outside of the clinical setting to generalize speech goals to functional communication. In this paper, we describe the development of technology that captures mixed speech signals in a group setting and allows the SLP to analyze the speech signals relative to treatment goals. The mixed speech signals are blindly separated into individual signals that are preprocessed before computation of loudness, pitch, shimmer, jitter, semitone standard deviation and sharpness. The proposed method has been previously validated on data obtained from clinical trials of people with Parkinson disease and healthy controls.