Segment-dependent dynamics in predicting Parkinson's disease

Williamson, James R.; Quatieri, Thomas F.; Helfer, Brian S.; Perricone, Joseph; Ghosh, Satrajit; Ciccarelli, Gregory; Mehta, Daryush D.

doi:10.21437/interspeech.2015-187

Cited by 19 publications

(11 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This results in a final accuracy of 95%. It is crucial to emphasize that, differently from standard dysarthria detection approaches [18,19,20], any specific training has been performed: we directly infer the speaker health status from the similarity coefficients α α α that provide us an interpretable model. Note that none of existing SA methods are able to also perform dysarthria detection.…”

Section: The α α α Weight and The Dysarthria Detectionmentioning

confidence: 99%

Interpretable Dysarthric Speaker Adaptation based on Optimal-Transport

Turrisi¹,

Badino²

2022

Preprint

View full text Add to dashboard Cite

This work addresses the mismatch problem between the distribution of training data (source) and testing data (target), in the challenging context of dysarthric speech recognition. We focus on Speaker Adaptation (SA) in command speech recognition, where data from multiple sources (i.e., multiple speakers) are available. Specifically, we propose an unsupervised Multi-Source Domain Adaptation (MSDA) algorithm based on optimal-transport, called MSDA via Weighted Joint Optimal Transport (MSDA-WJDOT). We achieve a Command Error Rate relative reduction of 16% and 7% over the speakerindependent model and the best competitor method, respectively. The strength of the proposed approach is that, differently from any other existing SA method, it offers an interpretable model that can also be exploited, in this context, to diagnose dysarthria without any specific training. Indeed, it provides a closeness measure between the target and the source speakers, reflecting their similarity in terms of speech characteristics. Based on the similarity between the target speaker and the healthy/dysarthric source speakers, we then define the healthy/dysarthric score of the target speaker that we leverage to perform dysarthria detection. This approach does not require any additional training and achieves a 95% accuracy in the dysarthria diagnosis.

show abstract

Section: The α α α Weight and The Dysarthria Detectionmentioning

confidence: 99%

Interpretable Dysarthric Speaker Adaptation based on Optimal-Transport

Turrisi¹,

Badino²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It has been suggested that changes in complexity in movement dimension-ality can reflect subtle physiological change as it manifests over time [21]. This has been used in several studies to relate coordination patterns with changes in neurological state and motor capability [21][22][23][24][25][26].…”

Section: E Correlation Structure Featuresmentioning

confidence: 99%

Using Body-worn Accelerometers to Detect Physiological Changes During Periods of Blast Overpressure Exposure

Williamson

Kim

Halford³

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

Self Cite

View full text Add to dashboard Cite

Repetitive exposure to non-concussive blast exposure may result in sub-clinical neurological symptoms. These changes may be reflected in the neural control gait and balance. In this study, we collected body-worn accelerometry data on individuals who were exposed to repetitive blast overpressures as part of their occupation. Accelerometry features were generated within periods of low-movement and gait. These features were the eigenvalues of high-dimensional correlation matrices, which were constructed with time-delay embedding at multiple delay scales. When focusing on the gait windows, there were significant correlations of the changes in features with the cumulative dose of blast exposure. When focusing on the lowmovement frames, the correlation with exposure were lower than that of the gait frames and statistically insignificant. In a cross-validated model, the overpressure exposure was predicted from gait features alone. The model was statistically significant and yielded an RMSE of 1.27 dB. With continued development, the model may be used to assess the physiological effects of repetitive blast exposure and guide training procedures to minimize impact on the individual.Clinical Relevance-This paper provides evidence to suggest that there is signal in body-worn accelerometry that indicates changes in gait across periods of repetitive exposure to blast overpressures. Further investigation is needed to understand the extent of the impact of repetitive overpressure exposures on the brain.

show abstract

“…Earlier work using ASR for pathological speech assessment has already been carried out in the context of other acoustic features. In [19,20], phoneme statistics, duration and confidence measures derived from off-the-shelf Spanish ASR systems were applied to speech assessment of Spanish-speaking patients with PD. In [21], a Cantonese ASR system was used to generate utterance-level posterior related features for broad phoneme classes in voice disorders assessment.…”

Section: A Related Workmentioning

confidence: 99%

“…This is a burden on clinical work, and also limits the scalability of automated patient screening and follow-up using clinically interpretable features. However, recent technical developments in automatic speech recognition (ASR) have been successfully involved in feature extraction in automatic assessment of various types of pathological speech [19][20][21]. This raises the question whether analysis of vowel articulation could also be fully automated in order to support clinical practice.…”

Section: Introductionmentioning

confidence: 99%

“…Given this background, the main purpose of the present study is to develop an automatic and language-independent method for vowel articulation measurement in terms of acoustic features. Inspired by the recent successes of using ASR for feature extraction in automatic pathological speech assessment [19][20][21], a universal phone/phoneme recognizer is adopted to detect speech frames representative of corner vowel articulation, followed by statistical analysis of the formant frequencies across the detected frames.…”

Section: Introductionmentioning

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

Preprint

View full text Add to dashboard Cite

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.

show abstract

Segment-dependent dynamics in predicting Parkinson's disease

Cited by 19 publications

References 22 publications

Interpretable Dysarthric Speaker Adaptation based on Optimal-Transport

Interpretable Dysarthric Speaker Adaptation based on Optimal-Transport

Using Body-worn Accelerometers to Detect Physiological Changes During Periods of Blast Overpressure Exposure

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

Contact Info

Product

Resources

About