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

Yuanyuan Liu*, Nelly Penttila, Tiina Ihalainen, Juulia Lintula, Rachel Convey, Okko Rasanen

*Corresponding author for this work

    Research output: Contribution to journalArticleScientificpeer-review

    13 Citations (Scopus)
    175 Downloads (Pure)

    Abstract

    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.

    Original languageEnglish
    Article number9463767
    Pages (from-to)2228-2243
    Number of pages16
    JournalIEEE/ACM Transactions on Audio Speech and Language Processing
    Volume29
    DOIs
    Publication statusPublished - 2021
    MoE publication typeA1 Journal article-refereed

    Keywords

    • automatic corner vowels detection
    • dysarthria
    • Parkinson's diseases
    • phoneme recognition
    • vowel articulation

    Fingerprint

    Dive into the research topics of 'Language-Independent Approach for Automatic Computation of Vowel Articulation Features in Dysarthric Speech Assessment'. Together they form a unique fingerprint.

    Cite this