Vowel articulation index and conversational spontaneous speech intelligibility in Parkinson's disease

Rountrey, Carrie; Molett, M.

doi:10.1016/j.parkreldis.2020.06.444

Cited by 2 publications

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“…These features are computed from formants of corner vowels /a/, /i/ and /u/, which are most commonly used in human languages and represent the extreme positions of the speaker's articulatory vowel working space [7,12]. These acoustic metrics have been used by speech and language pathologists (SLP) to study speech development, vowel identity and speaker characteristics in disordered speech [12], such as after stroke [26], in cerebral palsy [7] or in PD [27].…”

Section: A Related Workmentioning

confidence: 99%

“…In the latest studies, the vowel articulation features have been used solely, like in [28] where researchers evaluated articulation with VSA in patients with oral cancer, or together, like in [29] where VAI and FCR were utilized to study age and sex effects in European Portuguese vowels. As vowel articulation deficits depend much on the complexity of the speech task [16], VAI was studied in conversational spontaneous PD speech in [27].…”

Section: A Related Workmentioning

confidence: 99%

“…Different vowel space measurements (e.g. VAI and VSA) are still conducted manually in the field of SLP [27,30]. Therefore, it is important to automate tools, such as VAI and VSA, to improve validity and reliability in assessing and identifying communication disorders.…”

Section: A Related Workmentioning

confidence: 99%

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Language-Independent Approach for Automatic Computation of Vowel Articulation Features in Dysarthric Speech Assessment

Liu

Penttilä

Ihalainen

et al. 2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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Dysarthria is a common symptom for people with Parkinson's disease (PD), which affects respiration, phonation, articulation and prosody, and reduces the speech intelligibility as a result. Imprecise vowel articulation can be observed in people with PD. Acoustic features measuring vowel articulation have been demonstrated to be effective indicators of PD in its detection and assessment. Standard clinical vowel articulation features include the first two formants of the three corner vowels /a/, /i/ and /u/, from which clinically relevant parameters such as vowel working space area (VSA), vowel articulation index (VAI) and formants centralization ratio (FCR) are derived. Conventionally, manual annotation of the corner vowels from speech data is required before measuring vowel articulation. This process is time-consuming and requires specific expertise in speech signal analysis. The present work aims to reduce human effort in clinical analysis of PD speech by proposing an automatic pipeline for vowel articulation assessment. The method is based on automatic corner vowel detection using a language universal phoneme recognizer, followed by statistical analysis of the formant data. The approach removes the restrictions of prior knowledge of speaking content and the language in question. Experimental results on a Finnish PD speech corpus demonstrate the efficacy and reliability of the proposed method in deriving VAI, VSA, FCR and F2i/F2u (the second formant ratio for vowels /i/ and /u/) scores in a fully automated manner. The automatically computed parameters are shown to be highly correlated with features computed with manual annotations of corner vowels. In addition, automatically and manually computed vowel articulation features have comparable correlations with experts' ratings on speech intelligibility, voice impairment and overall severity of communication disorder. Language-independence of the proposed approach is further validated on a Spanish PD database, PC-GITA, as well as on TORGO corpus of English dysarthric speech. Results from these two corpora further demonstrate the efficacy of the automated features in separating PD/dysarthric speakers from controls, and that the features are correlated with Parkinson's disease severity ratings on PC-GITA and with level of dysarthria on TORGO.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Language-Independent Approach for Automatic Computation of Vowel Articulation Features in Dysarthric Speech Assessment

Liu

Penttilä

Ihalainen

et al. 2021

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…In fact, a variety of acoustic metrics can help identify alterations in the productions of PD compared to HC speakers. In some studies, vowels metrics are calculated to identify a possible marker of the progression of the disease in PD ( Sapir et al, 2010 ; Skodda et al, 2012 ; Rusz et al, 2013 ; Rountrey and Molett, 2020 ). The tVSA is one of the most frequently used acoustic indicator for the evaluation of imprecision in vowel production, as it can reflect major changes in articulatory movements in speech disorders.…”

Section: Introductionmentioning

confidence: 99%

Vowel production: a potential speech biomarker for early detection of dysarthria in Parkinson’s disease

Roland,

Huet,

Harmegnies

et al. 2023

Front. Psychol.

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ObjectivesOur aim is to detect early, subclinical speech biomarkers of dysarthria in Parkinson’s disease (PD), i.e., systematic atypicalities in speech that remain subtle, are not easily detectible by the clinician, so that the patient is labeled “non-dysarthric.” Based on promising exploratory work, we examine here whether vowel articulation, as assessed by three acoustic metrics, can be used as early indicator of speech difficulties associated with Parkinson’s disease.Study designThis is a prospective case–control study.MethodsSixty-three individuals with PD and 35 without PD (healthy controls-HC) participated in this study. Out of 63 PD patients, 43 had been diagnosed with dysarthria (DPD) and 20 had not (NDPD). Sustained vowels were recorded for each speaker and formant frequencies were measured. The analyses focus on three acoustic metrics: individual vowel triangle areas (tVSA), vowel articulation index (VAI) and the Phi index.ResultstVSA were found to be significantly smaller for DPD speakers than for HC. The VAI showed significant differences between these two groups, indicating greater centralization and lower vowel contrasts in the DPD speakers with dysarhtria. In addition, DPD and NDPD speakers had lower Phi values, indicating a lower organization of their vowel system compared to the HC. Results also showed that the VAI index was the most efficient to distinguish between DPD and NDPD whereas the Phi index was the best acoustic metric to discriminate NDPD and HC.ConclusionThis acoustic study identified potential subclinical vowel-related speech biomarkers of dysarthria in speakers with Parkinson’s disease who have not been diagnosed with dysarthria.

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Vowel articulation index and conversational spontaneous speech intelligibility in Parkinson's disease

Cited by 2 publications

References 0 publications

Language-Independent Approach for Automatic Computation of Vowel Articulation Features in Dysarthric Speech Assessment

Language-Independent Approach for Automatic Computation of Vowel Articulation Features in Dysarthric Speech Assessment

Vowel production: a potential speech biomarker for early detection of dysarthria in Parkinson’s disease

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