Detect the signs of dementia faster, more easily, and more objectively through speech with the help of artificial intelligence.
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Analysis of spontaneous speech data elicited through Cookie Theft picture description (1-5 minute samples) from 240 people probable AD category and 233 healthy controls. Linguistic and acoustic variables used to train a machine learning classifier to distinguish between the groups, with 82% accuracy.
Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2016). Linguistic features identify Alzheimer’s disease in narrative speech. Journal of Alzheimer's Disease, 49(2), 407-422.
A temporal Bayes network trained on 182 lexicosyntactic, 210 acoustic, and 85 semantic features extracted from 393 spontaneous speech samples elicited through Cookie Theft picture description can predict MMSE scores with a mean absolute error of 3.8, comparable to within-subject interrater (clinician) standard deviation of 3.9 to 4.8.
Yancheva, M., Fraser, K., & Rudzicz, F. (2015). Using Linguistic Features Longitudinally to Predict Clinical Scores for Alzheimer's Disease and Related Dementias.
Syntactic and semantic features were automatically extracted from transcriptions of narrative speech for three groups: semantic dementia (SD), progressive nonfluent aphasia (PNFA), and healthy controls. Machine learning classifiers trained on these features were able to distinguish between the three participant groups with up to 100% accuracy.
Fraser, K.C., Meltzer, J.A., Graham, N.L., Leonard, C., Hirst, G., Black, S.E., & Rochon, E. (2014). Automated classification of primary progressive aphasia subtypes from narrative speech transcripts. Cortex, 55, 43-60.Drug development in Alzheimer's disease and dementia is hampered by our ability to identify at risk groups before the onset of clinically significant symptoms. Winterlight Labs is addressing this problem by pioneering a speech-based AI technology which could help accurately predict risk for dementia years before a clinical diagnosis is obtained. Our technology can help detect and monitor subtle changes in cognition by assessing individuals more frequently and more objectively than the assessments used today.
The assessment is fast and easy to administer: the patient only needs to describe a picture or recall words as instructed by an iPad. Traditional cognitive assessments like the MMSE and MoCA are stressful for the patient and can be undermined by learning effects. Our assessment however, is designed to be administered frequently (as often as monthly) giving a more granular picture of changes in cognition over time. As a result our assessment can be routinely completed to monitor cognitive health and track variables that might be impacted by treatment.
The assessment can be completed objectively across patients and administrators. Standard cognitive assessments include components which are subjectively scored and often too coarse. We analyze hundreds of linguistic cues and vocal biomarkers to detect Alzheimer’s disease and other conditions with accuracies between 82% and 100%, as reported in our peer-reviewed academic studies.
Our analysis captures both the acoustic and lexical properties of speech to produce a set of over 500 variables. These variables are then grouped into aggregate metrics such word-finding difficulties, syntactic complexity, lexical richness, discourse mapping and more. They can be compared with an existing metric like an MMSE score.
Zhu Z., Novikova J., Rudzicz F. (2018)
IRASL, Interpretability and Robustness in Audio, Speech and Language Workshop at NeurIPS 2018.
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Balagopalan A., Novikova J., Rudzicz F., Ghassemi M. (2018)
ML4H, Machine Learning for Health Workshop at NeurIPS 2018.
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Pou-Prom C., Rudzicz F. (2018)
Proceedings of Empirical Methods in Natural Languages Processing 2018.
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Kokkinakis, D., Lundholm Fors, K., Fraser, K.C., and Nordlund, A. (2018)
Proceedings of the 11th edition of the Language Resources and Evaluation Conference (LREC). Miyazaki, Japan.
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Noorian Z., Pou-Prom C., Rudzicz F (2017)
ML4H, Machine Learning for Health Workshop at NIPS 2017.
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Chinaei H, Danks A, Mehta T, Chan-Currie L, Lin H, Rudzicz F (2017)
Computational Linguistics, 43(2):377-406.
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Yancheva, M., Rudzicz, F. (2016)
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
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Winterlight Labs
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