Dysarthria

About: Dysarthria is a research topic. Over the lifetime, 2402 publications have been published within this topic receiving 56554 citations.

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

Abstract: 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%.

Utility of the Intelligibility in Context Scale for Predicting Speech Intelligibility of Children with Cerebral Palsy

Abstract: The Intelligibility in Context Scale (ICS) is a widely used, efficient tool for describing a child's speech intelligibility. Few studies have explored the relationship between ICS scores and transcription intelligibility scores, which are the gold standard for clinical measurement. This study examined how well ICS composite scores predicted transcription intelligibility scores among children with cerebral palsy (CP), how well individual questions from the ICS differentially predicted transcription intelligibility scores, and how well the ICS composite scores differentiated between children with and without speech motor impairment. Parents of 48 children with CP, who were approximately 13 years of age, completed the ICS. Ninety-six adult naive listeners provided orthographic transcriptions of children's speech. Transcription intelligibility scores were regressed on ICS composite scores and individual item scores. Dysarthria status was regressed on ICS composite scores. Results indicated that ICS composite scores were moderately strong predictors of transcription intelligibility scores. One individual ICS item differentially predicted transcription intelligibility scores, and dysarthria severity influenced how well ICS composite scores differentiated between children with and without speech motor impairment. Findings suggest that the ICS has potential clinical utility for children with CP, especially when used with other objective measures of speech intelligibility.

Differentiation of Motor Speech Disorders through the Seven Deviance Scores from MonPaGe-2.0.s

Abstract: For the clinical assessment of motor speech disorders (MSDs) in French, the MonPaGe-2.0.s protocol has been shown to be sensitive enough to diagnose mild MSD based on a combination of acoustic and perceptive scores. Here, we go a step further by investigating whether these scores—which capture deviance on intelligibility, articulation, voice, speech rate, maximum phonation time, prosody, diadochokinetic rate—contribute to the differential diagnosis of MSDs. To this aim, we trained decision trees for two-class automatic classification of different pairs of MSD subtypes based on seven deviance scores that are computed in MonPaGe-2.0.s against matched normative data. We included 60 speakers with mild to moderate MSD from six neuropathologies (amyotrophic lateral sclerosis, Wilson, Parkinson and Kennedy disease, spinocerebellar ataxia, post-stroke apraxia of speech). The two-class classifications relied mainly on deviance scores from four speech dimensions and predicted with over 85% accuracy the patient’s correct clinical category for ataxic, hypokinetic and flaccid dysarthria; classification of the other groups (apraxia of speech and mixed dysarthria) was slightly lower (79% to 82%). Although not perfect and only tested on small cohorts so far, the classification with deviance scores based on clinically informed features seems promising for MSD assessment and classification.

Speaker Identity Preservation in Dysarthric Speech Reconstruction by Adversarial Speaker Adaptation

Abstract: Dysarthric speech reconstruction (DSR), which aims to improve the quality of dysarthric speech, remains a challenge, not only because we need to restore the speech to be normal, but also must preserve the speaker’s identity. The speaker representation extracted by the speaker encoder (SE) optimized for speaker verification has been explored to control the speaker identity. However, the SE may not be able to fully capture the characteristics of dysarthric speakers that are previously unseen. To address this research problem, we propose a novel multi-task learning strategy, i.e., adversarial speaker adaptation (ASA). The primary task of ASA fine-tunes the SE with the speech of the target dysarthric speaker to effectively capture identityrelated information, and the secondary task applies adversarial training to avoid the incorporation of abnormal speaking patterns into the reconstructed speech, by regularizing the distribution of reconstructed speech to be close to that of reference speech with high quality. Experiments show that the proposed approach can achieve enhanced speaker similarity and comparable speech naturalness with a strong baseline approach. Compared with dysarthric speech, the reconstructed speech achieves 22.3% and 31.5% absolute word error rate reduction for speakers with moderate and moderate-severe dysarthria respectively. Our demo page is released here1.

Dysarthria subgroups in talkers with Huntington's disease: Free classification versus feature-constrained classification

Abstract: In our recent study (Diehl et al., 2019), we examined the perceptual speech features of 48 talkers with Huntington's disease (HD) using the classic feature-based dysarthria rating scale (Darley et al., 1969). A cluster analysis based on speech feature ratings revealed four dysarthria subgroups within our cohort of talkers with HD. Talkers within each subgroup shared deviant speech features that set them apart from talkers with HD in other subgroups. Presumably, talkers with similar patterns of deviant speech features sound alike. In the current study, we will test this notion using a free classification approach. Specifically, 20 naive listeners will be asked to sort the speech samples of 48 talkers with HD that were used in the previous study into similar-sounding groups. Each listener's grouping decision will be submitted to an additive similarity tree cluster analysis to determine dysarthria subgroups based on the free classification task. Moreover, subgroup-specific speech features will be determined based on the previously established perceptual ratings of the classic dysarthria rating scale. Study outcomes will provide insights into the saliency of specific perceptual speech features in talkers with HD.