Automated Dysarthria Severity Classification: A Study on Acoustic Features and Deep Learning Techniques

Abstract

Assessing the severity level of dysarthria can provide an insight into the patient's improvement, assist pathologists to plan therapy, and aid automatic dysarthric speech recognition systems. In this article, we present a comparative study on the classification of dysarthria severity levels using different deep learning techniques and acoustic features. First, we evaluate the basic architectural choices such as deep neural network (DNN), convolutional neural network, gated recurrent units and long short-term memory network using the basic speech features, namely, Mel-frequency cepstral coefficients (MFCCs) and constant-Q cepstral coefficients. Next, speech-disorder specific features computed from prosody, articulation, phonation and glottal functioning are evaluated on DNN models. Finally, we explore the utility of low-dimensional feature representation using subspace modeling to give i-vectors, which are then classified using DNN models. Evaluation is done using the standard UA-Speech and TORGO databases. By giving an accuracy of 93.97% under the speaker-dependent scenario and 49.22% under the speaker-independent scenario for the UA-Speech database, the DNN classifier using MFCC-based i-vectors outperforms other systems.

A New Neurological Screening Approach for Diagnosing Brainstem Infarction Using the Calling Method and Familiar Voices

“…Additionally, the development of evaluation methods that are user-friendly for non-professionals would be necessary. Third, it is important to acknowledge the potential role of artificial intelligence (AI) in neurological assessments [ 18 , 19 ]. With advancements in machine learning techniques, AI has the ability to detect subtle changes in speech characteristics that may indicate neurological conditions like dysarthria.…”

A store-and-forward cloud-based telemonitoring system for automatic assessing dysarthria evolution in neurological diseases from video-recording analysis

“…For alleviating the above-mentioned limitations, a number of supportive systems to assess dysarthria via patient's speech or vocalperformance analysis have been proposed in literature. Examples include [12,25,26] which investigate the feasibility of machine learning (ML) methods for the analysis of audio-data collected in hospital and home scenarios. In [27,28], a telemonitoring-based application is introduced to automatically assess the evolution in the intelligibility of the speech of dysarthric patients.…”

“…To search for new quantitative outcome measures to assess dysarthria progress, different approaches were proposed. These mainly monitor the speech and vocal features of dysarthric subjects, both in home and hospital scenarios [7,[10][11][12][13][14]. However, as stated in [15,16], also the assessment of orofacial motor functions related to speech (or motor speech assessment) should be considered to: (i) detect subtle improvements or worsening in patients' conditions (especially for those who suffer from ALS, spinal muscular atrophy (SMA), facial palsy and stroke); (ii) evaluate pharmacological and non-pharmacological treatment progress and (iii) improve the staging of the rehabilitative strategies management and pursue an augmentative communication (AAC) assessment [1].…”

AFM signal model for dysarthric speech classification using speech biomarkers

“…These manifestations include diminished vocal volume, imprecise articulation, disturbances in coordinating respiratory and phonatory subsystems, and the presence of irregular speech pauses. The amalgamation of these defining attributes underscores the multifaceted nature of this speech disorder (Joshy and Rajan, 2022 ).…”