A Portable Automatic PA-TA-KA Syllable Detection System to Derive Biomarkers for Neurological Disorders

摘要

Neurological disorders disrupt brain functions, affecting the life of many individuals. Conventional neurological disorder diagnosis methods require inconvenient and expensive devices. Several studies have identified speech biomarkers that are informative of neurological disorders, so speech-based interfaces can provide effective, convenient and affordable prescreening tools for diagnosis. We have investigated stand-alone automatic speech-based assessment tools for portable devices. Our current data collection protocol includes seven brief tests for which we have developed specialized automatic speech recognition (ASR) systems. The most challenging task from an ASR perspective is a popular diadochokinetic test consisting of fast repetitions of "PA-TA-KA", where subjects tend to alter, replace, insert or skip syllables. This paper presents our efforts to build a speech-based application specific for this task, where the computation is fast, efficient, and accurate on a portable device, not in the cloud. The tool recognizes the target syllables, providing phonetic alignment. This information is crucial to reliably estimate biomarkers such as the number of repetitions, insertions, mispronunciations, and temporal prosodic structure of the repetitions. We train and evaluate the application for two neurological disorders: traumatic brain injuries (TBIs) and Parkinson's disease. The results show low syllable error rates and high boundary detection, across populations.