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.
Highlights
P ARKINSON’S disease (PD), the second most common neurodegenerative disease, has a wide range of symptoms, including characteristic movement disorders and non-motor symptoms on sleep, mental and cognitive performance [1]
The analysis shows that vowel articulation index (VAI) and formants centralization ratio (FCR) estimates are moderately correlated with the ratings, whereas vowel working space area (VSA) turns out to be less informative
We demonstrated on a Finnish PD corpus that vowel articulation features computed with automatic speech frame selection have strong correlations with the same features computed using manual annotations
Summary
P ARKINSON’S disease (PD), the second most common neurodegenerative disease, has a wide range of symptoms, including characteristic movement disorders and non-motor symptoms on sleep, mental and cognitive performance [1]. The manifestations of hypokinetic dysarthria can include impaired phonation, imprecise articulation, reduced variability of pitch and loudness, and other prosodic disturbances related to speech rate, stress and pauses [2, 5]. Due to these deficits, speech intelligibility of people with PD could be degraded. Previous studies show that articulation disorders can be quantitatively measured with acoustic analysis, which serves as a reliable, objective and non-invasive tool for detection and progression monitoring of PD [2, 6] In this context, vowel articulation in PD speech has attracted researchers’ attention [7, 8], since vowel clarity has been shown to be a powerful indicator of speech intelligibility [9, 10]
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