The Atlanta Motor Speech Disorders Corpus: Motivation, Development, and Utility

Laures‐Gore, Jacqueline; Russell, Scott M.; Patel, Rupal; Frankel, Michael

doi:10.1159/000448891

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…The dysarthric speech samples of the remaining four speakers have been collected from the database developed by the University of Illinois, Chicago [23]. Like the other databases, this database contains speech samples of the flaccid, hypokinetic, spastic, and ataxic kinds of dysarthria that have commonly used words and numerals [24]- [26]. A summary of the recorded database, including the speaker's age, gender, type of dysarthria, and severity, is provided in Table 1.…”

Section: Selection Of Parametersmentioning

confidence: 99%

Improving the intelligibility of dysarthric speech using a time domain pitch synchronous-based approach

Giri

Rayavarapu

2023

IJECE

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Dysarthria is a motor speech impairment that reduces the intelligibility of speech. Observations indicate that for different types of dysarthria, the fundamental frequency, intensity, and speech rate of speech are distinct from those of unimpaired speakers. Therefore, the proposed enhancement technique modifies these parameters so that they fall in the range for unimpaired speakers. The fundamental frequency and speech rate of dysarthric speech are modified using the time domain pitch synchronous overlap and add (TD-PSOLA) algorithm. Then its intensity is modified using the fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT)-based approach. This technique is applied to impaired speech samples of ten dysarthric speakers. After enhancement, the intelligibility of impaired and enhanced dysarthric speech is evaluated. The change in the intelligibility of impaired and enhanced dysarthric speech is evaluated using the rating scale and word count methods. The improvement in intelligibility is significant for speakers whose original intelligibility was poor. In contrast, the improvement in intelligibility was minimal for speakers whose intelligibility was already high. According to the rating scale method, for diverse speakers, the change in intelligibility ranges from 9% to 53%. Whereas, according to the word count method, this change in intelligibility ranges from 0% to 53%.

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Section: Selection Of Parametersmentioning

confidence: 99%

Improving the intelligibility of dysarthric speech using a time domain pitch synchronous-based approach

Giri

Rayavarapu

2023

IJECE

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“…However, the UA-Speech dataset is not sufficiently diverse to create a clinical dysarthria detection tool: the majority of speakers have spastic dysarthria originating from cerebral palsy. To expand the applicability of the proposed tool, the models trained on UA-Speech are tested on the Atlanta Motor Speech Disorder Corpus (AMSDC) [19], containing more dysarthria subtypes and etiology. Without speech from healthy controls, the AMSDC could not be used to train the detection model.…”

Section: Databasesmentioning

confidence: 99%

“…The Atlanta Motor Speech Disorders Corpus (AMSDC) contains speech recordings of 99 adults local to the South-Eastern US with acquired neurogenic disorders that resulted in a motor speech disorder [19]. Participants presented with aprosodia, dysarthria, and apraxia of speech.…”

Section: Atlanta Motor Speech Disorders Corpus (Amsdc)mentioning

confidence: 99%

Cross-Database Models for the Classification of Dysarthria Presence

et al. 2017

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Dysarthria is a motor speech disorder that impacts verbal articulation and coordination , resulting in slow, slurred and imprecise speech. Automated classification of dysarthria subtypes and severities could provide a useful clinical tool in assessing the onset and progress in treatment. This study represents a pilot project to train models to detect the presence of dysarthria in continuous speech. Subsets of the Universal Access Research Dataset (UA-Speech) and the Atlanta Motor Speech Disorders Corpus (AMSDC) database were utilized in a cross-database training strategy (training on UA-Speech / testing on AMSDC) to distinguish speech with and without dysarthria. In addition to traditional spectral and prosodic features, the current study also includes features based on the Teager Energy Operator (TEO) and the glottal waveform. Baseline results on the UA-Speech dataset maximize wordand participant-level accuracies at 75.3% and 92.9% using prosodic features. However, the cross-training of UA-Speech tested on the AMSDC maximize word-and participant-level accuracies at 71.3% and 90% based on a TEO feature. The results of this pilot study reinforce consideration of dysarthria subtypes in cross-dataset training as well as highlight additional features that may be sensitive to the presence of dysarthria in continuous speech.

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“…Even when permission for collecting and using data by patients with aphasia has been obtained, considerable resources are required to move patients through the steps of consenting, screening and testing. A solution to this problem could be data sharing, as is increasingly realized in recent bibliography, which has evidenced a surge in corpora of language datasets from speakers with various disorders, including aphasia, in several languages such as Dutch (Westerhout and Monachesi, 2007 ), Cantonese (Kong and Law, 2019 ), Russian (Khudyakova et al, 2016 ), Croatian (Kuvač Kraljević et al, 2017 ), and, of course, English (Mirman et al, 2010 ; Williams et al, 2010 ; MacWhinney et al, 2011 ; Laures-Gore et al, 2016 ). Despite such attempts of developing corpora widely available to researchers, the need for additional open data banks from different languages still remains.…”

Section: Introductionmentioning

confidence: 99%