TY - JOUR
T1 - Language-Independent Approach for Automatic Computation of Vowel Articulation Features in Dysarthric Speech Assessment
AU - Liu, Yuanyuan
AU - Penttilä, Nelly
AU - Ihalainen, Tiina
AU - Lintula, Juulia
AU - Convey, Rachel
AU - Räsänen, Okko
N1 - Funding Information:
Manuscript received August 26, 2020; revised March 26, 2021 and May 31, 2021; accepted June 15, 2021. Date of publication June 23, 2021; date of current version July 14, 2021. This work was supported by the Academy of Finland under Grant 314602. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Mathew Magimai Doss. (Corresponding author: Yuanyuan Liu.) Yuanyuan Liu is with the Unit of Computing Sciences, Tampere University, Tampere 33720, Pirkanmaa, Finland (e-mail: yuanyuan.liu@tuni.fi).
Publisher Copyright:
© 2014 IEEE.
PY - 2021
Y1 - 2021
N2 - Imprecise vowel articulation can be observed in people with Parkinson's disease (PD). Acoustic features measuring vowel articulation have been demonstrated to be effective indicators of PD in its assessment. Standard clinical vowel articulation features of vowel working space area (VSA), vowel articulation index (VAI) and formants centralization ratio (FCR), are derived the first two formants of the three corner vowels /a/, /i/ and /u/. Conventionally, manual annotation of the corner vowels from speech data is required before measuring vowel articulation. This process is time-consuming. 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 automatic method in deriving VAI, VSA, FCR and F2i/F2u (the second formant ratio for vowels /i/ and /u/). 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.
AB - Imprecise vowel articulation can be observed in people with Parkinson's disease (PD). Acoustic features measuring vowel articulation have been demonstrated to be effective indicators of PD in its assessment. Standard clinical vowel articulation features of vowel working space area (VSA), vowel articulation index (VAI) and formants centralization ratio (FCR), are derived the first two formants of the three corner vowels /a/, /i/ and /u/. Conventionally, manual annotation of the corner vowels from speech data is required before measuring vowel articulation. This process is time-consuming. 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 automatic method in deriving VAI, VSA, FCR and F2i/F2u (the second formant ratio for vowels /i/ and /u/). 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.
KW - automatic corner vowels detection
KW - dysarthria
KW - Parkinson's diseases
KW - phoneme recognition
KW - vowel articulation
U2 - 10.1109/TASLP.2021.3090973
DO - 10.1109/TASLP.2021.3090973
M3 - Article
AN - SCOPUS:85110635031
VL - 29
SP - 2228
EP - 2243
JO - Ieee-Acm transactions on audio speech and language processing
JF - Ieee-Acm transactions on audio speech and language processing
SN - 2329-9290
ER -