Intelligibility Assessment of Dysarthric Speech

Abstract

Abstract newlineDysarthria is a neurogenic speech disorder and speakers with dysarthria newlineproduce less intelligible speech. Assessing the intelligibility is very much newlineessential to know about the progressiveness of the disease and to plan newlinethe therapy by speech language pathologists. Since subjective tests are newlineperceptual in nature, they are inconsistent, biased, time consuming and newlinecostly. In contrast, automatic objective assessment methods are repeatable, newlineless time consuming and relatively cheap. Hence, it is important to newlineautomate the process. newlineAutomation involves identification of features that can distinguish the newlineintelligibility levels followed by classification using machine learning techniques. newlineTypes of features depend on the signal representation. This newlinework aims at investigating different signal representations, features and newlinemachine learning techniques for automatic intelligibility assessment of newlinedysarthric speech. newlineFeatures distinguishing the intelligibility levels are identified and computed newlinefrom corresponding signal representations. These features are extracted newlineat frame level and utterance level which are known as local and newlineglobal descriptors respectively. To obtain a fixed length feature vector, newlinelocal descriptors are converted to global descriptors using temporal and newlineFisher vector encoding techniques. Global descriptors are given to PLDA newlineand ANN classifiers for intelligibility assessment. newlineAlso, different time-frequency representations with fixed and variable time newlineresolutions are derived. STFT and SFF are the time-frequency representations newlinewith fixed time resolution whereas, CQT is the variable time newlineresolution time-frequency representation. These derived time-frequency newlinerepresentations are used with CNN classifier. In addition to this, region newlineiii newlinebased prediction is employed to improve the performance. In the assessment newlinetask, two standard databases of American English language are newlineused. newlineThe speech data from UA (only words) and TORGO (both words and newlinesentences) databases are used. The performance of the extracted fe