TORONTO, February 26, 2019
The paper Detecting Cognitive Impairments by Agreeing on Interpretations of Linguistic Features (Zining Zhu, Jekaterina Novikova and Frank Rudzicz) was accepted into NAACL 2019.
The North American Chapter of the Association for Computational Linguistics (NAACL) is a top-tier conference in computational linguistics and artificial intelligence that will be hosted in Minneapolis, USA in early June.
Linguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or the number of linguistic features have been two applicable approaches. However, acquiring additional clinical data could be expensive, and hand-carving features are burdensome.
In this paper, we take a third approach, putting forward Consensus Networks (CN), a framework to classify after reaching agreements between modalities. We divide the linguistic features into non-overlapping subsets according to their modalities, let neural networks learn low-dimensional representations that agree with each other. These representations are passed into a classifier network. All neural networks are optimized iteratively. Overall, using 413 linguistic features, our models significantly outperform traditional classifiers, which are used by the state-of-the-art papers.
Winterlight Labs is commercializing a proprietary language-based diagnostic system that analyzes natural speech to detect and monitor dementia, Alzheimer’s, aphasia, and various other cognitive conditions. Winterlight's scalable platform uses short recorded speech samples to analyze hundreds of linguistic cues, and can detect dementia and other conditions with a high level of accuracy. This is a major improvement over current pencil-and-paper tests which are time-consuming, costly, and difficult to administer. The platform has applications in drug trials, long-term and primary care, and speech-language pathology.
Since its founding, Winterlight Labs has seen strong interest from pharmaceutical companies and other potential partners who view the technology as a major improvement over current methods of detection or screening. The company has gained support from the pan-Canadian AGE-WELL Network of Centres of Excellence (NCE), the Ontario Brain Institute, Ontario Centres of Excellence, and the University of Toronto Banting and Best Centre for Innovation and Entrepreneurship. For more information, visit www.winterlightlabs.com.
Liam Kaufman, CEO
liam [at] winterlightlabs.com