Characterizing Dysarthria Diversity for Automatic Speech Recognition: A Tutorial From the Clinical Perspective

Abstract

Despite significant advancements in automatic speech recognition (ASR) technology, even the best performing ASR systems are inadequate for speakers with impaired speech. This inadequacy may be, in part, due to the challenges associated with acquiring a sufficiently diverse training sample of disordered speech. Speakers with dysarthria, which refers to a group of divergent speech disorders secondary to neurologic injury, exhibit highly variable speech patterns both within and across individuals. This diversity is currently poorly characterized and, consequently, difficult to adequately represent in disordered speech ASR corpora. In this article, we consider the variable expressions of dysarthria within the context of established clinical taxonomies (e.g., Darley, Aronson, and Brown dysarthria subtypes). We also briefly consider past and recent efforts to capture this diversity quantitatively using speech analytics. Understanding dysarthria diversity from the clinical perspective and how this diversity may impact ASR performance could aid in (1) optimizing data collection strategies for minimizing bias; (2) ensuring representative ASR training sets; and (3) improving generalization of ASR for difficult-to-recognize speakers. Our overarching goal is to facilitate the development of robust ASR systems for dysarthric speech using clinical knowledge.

Ataxic speech disorders and Parkinson’s disease diagnostics via stochastic embedding of empirical mode decomposition

“…Dysarthria refers to a group of divergent SMDs often secondary to neurologic injury (but not limited to it) and exhibits highly variable speech patterns within and across individuals [ 10 ]. One of the most established clinical taxonomy for SMD corresponds to the Darley, Aronson, and Brown (DAB) model [ 11 ] that foresees 38 atypical speech features rated on a 7-point scale and groups dysarthria types based on speech feature profiles [ 10 ]. The DAB model split SMD into two classes, apraxia and dysarthria, and dysarthria into five clusters, flaccid, spastic, ataxic, hypokinetic, and hyperkinetic.…”

“…For example, there are ASR speaker-independent (SI) systems, trained on large multispeaker datasets, or ASR speaker-dependent (SD) systems, trained by an existing SI model to a target speaker or by a unique target speaker’s speech data [ 10 , 17 ]. Commercially developed SI have low error rates for healthy speakers but appear to perform considerably worse with speech impairments tasks [ 10 , 18 ]. Thus, extensive work has been conducted on SD systems for speech impairments showing stronger performances than SI [ 10 , 19 , 20 ].…”

“…Our work is built upon the following considerations. Firstly, we consider the speech taxonomy provided by the DAB model shown in Fig 1 (produced by [ 10 ]). Secondly, we consider Parkinson’s disease and aim to discriminate the presence or absence of such disorder by quantifying ataxic dysarthria or ataxic speech .…”

“…Patients often show a combination of the five subtypes (i.e., mixed dysarthria) independently of the final diagnosis, and no speech feature (or a set) has yet to be found discriminative of the different types [1,[12][13][14]. Furthermore, this clinical system relies entirely on subjective auditory-perceptual observations requiring advanced expert clinical training [10,13]. Automatic Speaker Recognition (ASR) represent the ideal tool for automatically detecting and monitoring the range of diversity in dysarthria symptoms.…”

“…For example, there are ASR speakerindependent (SI) systems, trained on large multispeaker datasets, or ASR speaker-dependent (SD) systems, trained by an existing SI model to a target speaker or by a unique target speaker's speech data [10,17]. Commercially developed SI have low error rates for healthy speakers but appear to perform considerably worse with speech impairments tasks [10,18]. Thus, extensive work has been conducted on SD systems for speech impairments showing stronger performances than SI [10,19,20].…”