Automatic assessment of dysarthria severity level using audio descriptors

Bhat, Chitralekha; Vachhani, Bhavik; Kopparapu, Sunil Kumar

doi:10.1109/icassp.2017.7953122

Cited by 46 publications

(17 citation statements)

References 11 publications

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“…Furthermore, isolated words are also considered in their dataset, whereas we only considered continuous speech. Similarly, the study in Reference [63] uses MFCCs along with delta features in a CNN classifier with the TORGO dataset and reaches a severity assessment accuracy of 98.3%. However, this study also excludes healthy speech data and only conducts testing on two speakers (one mild and one severe), whereas the current study examines all speakers for testing.…”

Section: Discussionmentioning

confidence: 99%

Prosody-Based Measures for Automatic Severity Assessment of Dysarthric Speech

2020

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One of the first cues for many neurological disorders are impairments in speech. The traditional method of diagnosing speech disorders such as dysarthria involves a perceptual evaluation from a trained speech therapist. However, this approach is known to be difficult to use for assessing speech impairments due to the subjective nature of the task. As prosodic impairments are one of the earliest cues of dysarthria, the current study presents an automatic method of assessing dysarthria in a range of severity levels using prosody-based measures. We extract prosodic measures related to pitch, speech rate, and rhythm from speakers with dysarthria and healthy controls in English and Korean datasets, despite the fact that these two languages differ in terms of prosodic characteristics. These prosody-based measures are then used as inputs to random forest, support vector machine and neural network classifiers to automatically assess different severity levels of dysarthria. Compared to baseline MFCC features, 18.13% and 11.22% relative accuracy improvement are achieved for English and Korean datasets, respectively, when including prosody-based features. Furthermore, most improvements are obtained with a better classification of mild dysarthric utterances: a recall improvement from 42.42% to 83.34% for English speakers with mild dysarthria and a recall improvement from 36.73% to 80.00% for Korean speakers with mild dysarthria.

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Section: Discussionmentioning

confidence: 99%

Prosody-Based Measures for Automatic Severity Assessment of Dysarthric Speech

2020

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“…The advancement in signal processing and ML literature has encouraged researchers to explore the use of signal processing and ML techniques for automatic speech intelligibility assessment. Broadly, these methods aim at measuring the abnormalities in spoken speech by extracting handcrafted acoustic features based on statistical signal processing [14,15] and/or supervised methods based on ML [16,17]. Such techniques offer the advantage of frequent, cost effective and objective assessment of speech intelligibility.…”

Section: Review Of Intelligibility Assessment Methodsmentioning

confidence: 99%

Automatic speaker independent dysarthric speech intelligibility assessment system

Tripathi

Bhosale

Kopparapu

2021

Computer Speech & Language

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Dysarthria is a condition which hampers the ability of an individual to control the muscles that play a major role in speech delivery. The loss of fine control over muscles that assist the movement of lips, vocal chords, tongue and diaphragm results in abnormal speech delivery. One can assess the severity level of dysarthria by analyzing the intelligibility of speech spoken by an individual. Continuous intelligibility assessment helps speech language pathologists not only study the impact of medication but also allows them to plan personalized therapy. It helps the clinicians immensely if the intelligibility assessment system is reliable, automatic, simple for (a) patients to undergo and (b) clinicians to interpret. Lack of availability of dysarthric data has resulted in development of speaker dependent automatic intelligibility assessment systems which requires patients to speak a large number of utterances. In this paper, we propose (a) a cost minimization procedure to select an optimal (small) number of utterances that need to be spoken by the dysarthric patient, (b) four different speaker independent intelligibility assessment systems which require the patient to speak a small number of words, and (c) the assessment score is close to the perceptual score that the Speech Language Pathologist (SLP) can relate to. The need for small number of utterances to be spoken by the patient and the score being relatable to the SLP benefits both the dysarthric patient and the clinician from usability perspective.

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“…This relatively small number is in contrast to the number of speakers in other areas of speech technology, such as speech recognition [72] and speaker verification [73], where it is possible to collect speech from healthy speakers by recording utterances from hundreds or even thousands of talkers. It should be noted, however, that in dysarthric speech assessment, a similar small number of speakers (less than 15) has been used also in previous studies [23][74] [75].…”

Section: The Ua-speech Databasementioning

confidence: 99%