Automatic recognition of unified Parkinson's disease rating from speech with acoustic, i-vector and phonotactic features

An, Guozhen; Brizan, David Guy; Ma, Min; Morales, Michelle; Syed, Ali; Rosenberg, Andrew

doi:10.21437/interspeech.2015-185

Cited by 15 publications

(2 citation statements)

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“…In [21], a Cantonese ASR system was used to generate utterance-level posterior related features for broad phoneme classes in voice disorders assessment. In connection with the practical limitation that a usable ASR system may not be available for the target language, language-mismatched speech recognizer was utilized to extract phonotactic and duration features, as well as probability features in [31]. However, to the best of our knowledge, there is no existing approach for automatic computation of the widely utilized vowel articulation features VSA, VAI, FCR or F2i/F2u.…”

Section: A Related Workmentioning

confidence: 99%

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

Liu,

Penttilä,

Ihalainen

et al. 2021

Preprint

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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.

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Section: A Related Workmentioning

confidence: 99%

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

Liu,

Penttilä,

Ihalainen

et al. 2021

Preprint

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show abstract

“…Speech signal has been demonstrated to be a valuable indicator of disease progression and treatment efficacy in PD [7]. There is a large body of research on automatic PD assessment using speech, which employs acoustic analysis and pattern recognition techniques and aims at objective, non-invasive, and cost-efficient health care technology for the benefit of clinical practice [8], [9], [10], [11], [12], [13], [14]. Most studies have investigated PD detection, which is typically formulated as a binary classification problem.…”

mentioning

confidence: 99%

Automatic Assessment of Parkinson's Disease Using Speech Representations of Phonation and Articulation

Liu

Reddy

Penttilä

et al. 2023

IEEE/ACM Trans. Audio Speech Lang. Process.

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Speech from people with Parkinson's disease (PD) are likely to be degraded on phonation, articulation, and prosody. Motivated to describe articulation deficits comprehensively, we investigated 1) the universal phonological features that model articulation manner and place, also known as speech attributes, and 2) glottal features capturing phonation characteristics. These were further supplemented by, and compared with, prosodic features using a popular compact feature set and standard MFCC. Temporal characteristics of these features were modeled by convolutional neural networks. Besides the features, we were also interested in the speech tasks for collecting data for automatic PD speech assessment, like sustained vowels, text reading, and spontaneous monologue. For this, we utilized a recently collected Finnish PD corpus (PDSTU) as well as a Spanish database (PC-GITA). The experiments were formulated as regression problems against expert ratings of PD-related symptoms, including ratings of speech intelligibility, voice impairment, overall severity of communication disorder on PDSTU, as well as on the Unified Parkinson's Disease Rating Scale (UPDRS) on PC-GITA. The experimental results show: 1) the speech attribute features can well indicate the severity of pathologies in parkinsonian speech; 2) combining phonation features with articulatory features improves the PD assessment performance, but requires high-quality recordings to be applicable; 3) read speech leads to more accurate automatic ratings than the use of sustained vowels, but not if the amount of speech is limited to correspond to the sustained vowels in duration; and 4) jointly using data from several speech tasks can further improve the automatic PD assessment performance.

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Voice Features Examination for Parkinson’s Disease Detection Utilizing Machine Learning Methods

Putri,

Mara,

Ismail

et al. 2024

Proceedings of the 4th International Conference on Electronics, Biomedical Engineering, and Health Informatics

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Automatic recognition of unified Parkinson's disease rating from speech with acoustic, i-vector and phonotactic features

Cited by 15 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

Automatic Assessment of Parkinson's Disease Using Speech Representations of Phonation and Articulation

Voice Features Examination for Parkinson’s Disease Detection Utilizing Machine Learning Methods

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