Vowel Contrast and Speech Intelligibility in Dysarthria

Kim, Heejin; Hasegawa‐Johnson, Mark; Perlman, Adrienne L.

doi:10.1159/000318881

Cited by 81 publications

(69 citation statements)

References 40 publications

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“…Increased acoustic vowel contrast is associated with improved speech intelligibility (e.g., Connaghan & Patel, 2017;Kim, Hasegawa-Johnson, & Perlman, 2011;Turner et al, 1995); hence, increased acoustic vowel contrast is highly desirable for these talkers.…”

Section: Discussionmentioning

confidence: 99%

Tongue- and Jaw-Specific Contributions to Acoustic Vowel Contrast Changes in the Diphthong /ai/ in Response to Slow, Loud, and Clear Speech

Mefferd

2017

J Speech Lang Hear Res

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Purpose This study sought to determine decoupled tongue and jaw displacement changes and their specific contributions to acoustic vowel contrast changes during slow, loud, and clear speech. Method Twenty typical talkers repeated “see a kite again” 5 times in 4 speech conditions (typical, slow, loud, clear). Speech kinematics were recorded using 3-dimensional electromagnetic articulography. Tongue composite displacement, decoupled tongue displacement, and jaw displacement during /ai/, as well as the distance between /a/ and /i/ in the F1–F2 vowel space, were examined during the diphthong /ai/ in “kite.” Results Displacements significantly increased during all 3 speech modifications. However, jaw displacements increased significantly more during clear speech than during loud and slow speech, whereas decoupled tongue displacements increased significantly more during slow speech than during clear and loud speech. In addition, decoupled tongue displacements increased significantly more during clear speech than during loud speech. Increases in acoustic vowel contrast tended to be larger during slow speech than during clear speech and were predominantly tongue-driven, whereas those during clear speech were fairly equally accounted for by changes in decoupled tongue and jaw displacements. Increases in acoustic vowel contrast during loud speech were smallest and were predominantly tongue-driven, particularly in men. Conclusions Findings suggest that task-specific patterns of decoupled tongue and jaw displacement change and task-specific patterns of decoupled tongue and jaw contributions to vowel acoustic change across these speech modifications. Clinical implications are discussed.

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Section: Discussionmentioning

confidence: 99%

Tongue- and Jaw-Specific Contributions to Acoustic Vowel Contrast Changes in the Diphthong /ai/ in Response to Slow, Loud, and Clear Speech

Mefferd

2017

J Speech Lang Hear Res

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“…Dysarthric speech is always with degraded speech intelligibility due to impaired voice and articulation functions [1][2][3]. For example, Parkinson's disease and amyotrophic lateral sclerosis (ALS) impact the patient's motor functions and therefore impair their speech.…”

Section: Introductionmentioning

confidence: 99%

Recognizing Dysarthric Speech due to Amyotrophic Lateral Sclerosis with Across-Speaker Articulatory Normalization

Hahm¹,

Heitzman²,

Wang³

2015

Proceedings of SLPAT 2015: 6th Workshop on Speech and Language Processing for Assistive Technologies

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Recent dysarthric speech recognition studies using mixed data from a collection of neurological diseases suggested articulatory data can help to improve the speech recognition performance. This project was specifically designed for the speakerindependent recognition of dysarthric speech due to amyotrophic lateral sclerosis (ALS) using articulatory data. In this paper, we investigated three across-speaker normalization approaches in acoustic, articulatory, and both spaces: Procrustes matching (a physiological approach in articulatory space), vocal tract length normalization (a data-driven approach in acoustic space), and feature space maximum likelihood linear regression (a model-based approach for both spaces), to address the issue of high degree of variation of articulation across different speakers. A preliminary ALS data set was collected and used to evaluate the approaches. Two recognizers, Gaussian mixture model (GMM) -hidden Markov model (HMM) and deep neural network (DNN) -HMM, were used. Experimental results showed adding articulatory data significantly reduced the phoneme error rates (PERs) using any or combined normalization approaches. DNN-HMM outperformed GMM-HMM in all configurations. The best performance (30.7% PER) was obtained by triphone DNN-HMM + acoustic and articulatory data + all three normalization approaches, a 15.3% absolute PER reduction from the baseline using triphone GMM-HMM + acoustic data.

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“…Thus, intelligibility is a product of the joint efforts of the speaker and the listener (Lindblom, 1990). Intelligibility has received considerable attention in the adult dysarthria literature (e.g., D'Innocenzo, Tjaden, & Greenman, 2006;Kim, Hasegawa-Johnson, & Perlman, 2011;Liss, Spitzer, Caviness, & Adler, 2002;McAuliffe, Carpenter, & Moran, 2010;Rong, Loucks, Kim, & Hasegawa-Johnson, 2012). Many variables have been shown to influence intelligibility, highlighting the complexity of the construct.…”

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confidence: 99%

“…These variables can be separated into those associated with the speaker, the listener, and contextual factors (Hustad & Weismer, 2007). Historically, research has focused on understanding how characteristics of the signal relate to intelligibility (see Kim et al, 2011;Turner, Tjaden, & Weismer, 1995;Weismer, Jeng, Laures, Kent, & Kent, 2001;Yunusova, Weismer, Kent, & Rusche, 2005). In recent years, efforts have focused on perceptual variables related to the listener in studies of intelligibility, including studies of perceptual learning in listeners when presented with dysarthric speech, as well as listening strategies and mechanisms Borrie, McAuliffe, Liss, O'Beirne, & Anderson, 2013;Choe, Liss, Azuma, & Mathy, 2012;Kim & Nanney, 2014).…”

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confidence: 99%

Variability and Diagnostic Accuracy of Speech Intelligibility Scores in Children

Hustad

Oakes

Allison

2015

J Speech Lang Hear Res

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a,bPurpose: We examined variability of speech intelligibility scores and how well intelligibility scores predicted group membership among 5-year-old children with speech motor impairment (SMI) secondary to cerebral palsy and an agematched group of typically developing (TD) children. Method: Speech samples varying in length from 1-4 words were elicited from 24 children with cerebral palsy (mean age 60.50 months) and 20 TD children (mean age 60.33 months). Two hundred twenty adult listeners made orthographic transcriptions of speech samples (n = 5 per child). Results: Variability associated with listeners made a significant contribution to explaining the variance in intelligibility scores for TD and SMI children, but the magnitude was greater for TD children. Intelligibility scores differentiated very well between children who have SMI and TD children when intelligibility was at or below approximately 75% and above approximately 85%. Conclusions: Intelligibility seems to be a useful clinical tool for differentiating between TD children and children with SMI at 5 years of age; however, there is considerable variability within and between listeners, highlighting the need for more than one listener per child to ensure validity of an intelligibility measure.

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Vowel Contrast and Speech Intelligibility in Dysarthria

Cited by 81 publications

References 40 publications

Tongue- and Jaw-Specific Contributions to Acoustic Vowel Contrast Changes in the Diphthong /ai/ in Response to Slow, Loud, and Clear Speech

Tongue- and Jaw-Specific Contributions to Acoustic Vowel Contrast Changes in the Diphthong /ai/ in Response to Slow, Loud, and Clear Speech

Recognizing Dysarthric Speech due to Amyotrophic Lateral Sclerosis with Across-Speaker Articulatory Normalization

Variability and Diagnostic Accuracy of Speech Intelligibility Scores in Children

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