Multilingual evaluation of interpretable biomarkers to represent language and speech patterns in Parkinson's disease

Favaro, Anna; Moro-Velázquez, Laureano; Butala, Ankur; Motley, Chelsie; Cao, Tianyu; Stevens, Robert; Villalba, Jesús; Dehak, Najim

doi:10.3389/fneur.2023.1142642

Cited by 10 publications

(3 citation statements)

References 82 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, machine learning (ML) techniques have been developed utilizing various speech and linguistic biomarkers to identify individuals with MCI ( 26 ), early AD ( 27 , 28 ), dementia ( 29 ), Parkinson’s disease ( 30 ), and frontotemporal disorders ( 31 ). For instance, Hajjar and colleagues ( 27 ) found both acoustic and lexical-semantic biomarkers to be sensitive to cognitive impairment and disease progression in the early stages of AD.…”

Section: Introductionmentioning

confidence: 99%

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population

Kaser,

Lacritz,

Winiarski

et al. 2024

Front. Neurol.

View full text Add to dashboard Cite

ObjectiveEarly detection of cognitive impairment in the elderly is crucial for diagnosis and appropriate care. Brief, cost-effective cognitive screening instruments are needed to help identify individuals who require further evaluation. This study presents preliminary data on a new screening technology using automated voice recording analysis software in a Spanish population.MethodData were collected from 174 Spanish-speaking individuals clinically diagnosed as cognitively normal (CN, n = 87) or impaired (mild cognitive impairment [MCI], n = 63; all-cause dementia, n = 24). Participants were recorded performing four common language tasks (Animal fluency, alternating fluency [sports and fruits], phonemic “F” fluency, and Cookie Theft Description). Recordings were processed via text-transcription and digital-signal processing techniques to capture neuropsychological variables and audio characteristics. A training sample of 122 subjects with similar demographics across groups was used to develop an algorithm to detect cognitive impairment. Speech and task features were used to develop five independent machine learning (ML) models to compute scores between 0 and 1, and a final algorithm was constructed using repeated cross-validation. A socio-demographically balanced subset of 52 participants was used to test the algorithm. Analysis of covariance (ANCOVA), covarying for demographic characteristics, was used to predict logistically-transformed algorithm scores.ResultsMean logit algorithm scores were significantly different across groups in the testing sample (p < 0.01). Comparisons of CN with impaired (MCI + dementia) and MCI groups using the final algorithm resulted in an AUC of 0.93/0.90, with overall accuracy of 88.4%/87.5%, sensitivity of 87.5/83.3, and specificity of 89.2/89.2, respectively.ConclusionFindings provide initial support for the utility of this automated speech analysis algorithm as a screening tool for cognitive impairment in Spanish speakers. Additional study is needed to validate this technology in larger and more diverse clinical populations.

show abstract

Section: Introductionmentioning

confidence: 99%

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population

Kaser,

Lacritz,

Winiarski

et al. 2024

Front. Neurol.

View full text Add to dashboard Cite

show abstract

“…These limitations are even more pronounced due to the different standardizations used for voice recordings, which include the type of microphone used as well as acoustic conditions 7 . In particular, phonetic variability across different languages imposes considerable practical challenges for developing a unified speech assessment framework 18 . This has motivated the scientific community to explore the possibility of adapting information from different languages to assess certain pathologies 19 , which has raised questions about informational deficits in language-dependent speech dimensions and features 20 .…”

Section: Introductionmentioning

confidence: 99%

“…This has motivated the scientific community to explore the possibility of adapting information from different languages to assess certain pathologies 19 , which has raised questions about informational deficits in language-dependent speech dimensions and features 20 . Indeed, some studies have shown that differences in language did not impact the clinical assessment of disease phenotypes 18 , 21 . Therefore, the development of cross-languages and/or cross-pathology models could be the way to find robust models, with high performance and sufficient generalization for voice-based pathology classification and monitoring.…”

Section: Introductionmentioning

confidence: 99%

Automatic speech-based assessment to discriminate Parkinson’s disease from essential tremor with a cross-language approach

Rios-Urrego,

Rusz,

Orozco-Arroyave

2024

npj Digit. Med.

View full text Add to dashboard Cite

Parkinson’s disease (PD) and essential tremor (ET) are prevalent movement disorders that mainly affect elderly people, presenting diagnostic challenges due to shared clinical features. While both disorders exhibit distinct speech patterns—hypokinetic dysarthria in PD and hyperkinetic dysarthria in ET—the efficacy of speech assessment for differentiation remains unexplored. Developing technology for automatic discrimination could enable early diagnosis and continuous monitoring. However, the lack of data for investigating speech behavior in these patients has inhibited the development of a framework for diagnostic support. In addition, phonetic variability across languages poses practical challenges in establishing a universal speech assessment system. Therefore, it is necessary to develop models robust to the phonetic variability present in different languages worldwide. We propose a method based on Gaussian mixture models to assess domain adaptation from models trained in German and Spanish to classify PD and ET patients in Czech. We modeled three different speech dimensions: articulation, phonation, and prosody and evaluated the models’ performance in both bi-class and tri-class classification scenarios (with the addition of healthy controls). Our results show that a fusion of the three speech dimensions achieved optimal results in binary classification, with accuracies up to 81.4 and 86.2% for monologue and /pa-ta-ka/ tasks, respectively. In tri-class scenarios, incorporating healthy speech signals resulted in accuracies of 63.3 and 71.6% for monologue and /pa-ta-ka/ tasks, respectively. Our findings suggest that automated speech analysis, combined with machine learning is robust, accurate, and can be adapted to different languages to distinguish between PD and ET patients.

show abstract

Interpretable speech features vs. DNN embeddings: What to use in the automatic assessment of Parkinson’s disease in multi-lingual scenarios

Favaro,

Tsai,

Butala

et al. 2023

Computers in Biology and Medicine

View full text Add to dashboard Cite

Multilingual evaluation of interpretable biomarkers to represent language and speech patterns in Parkinson's disease

Cited by 10 publications

References 82 publications

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population

A novel speech analysis algorithm to detect cognitive impairment in a Spanish population

Automatic speech-based assessment to discriminate Parkinson’s disease from essential tremor with a cross-language approach

Interpretable speech features vs. DNN embeddings: What to use in the automatic assessment of Parkinson’s disease in multi-lingual scenarios

Contact Info

Product

Resources

About