We leverage voice biomarkers and machine learning models to detect and predict various neurodegenerative diseases. Our current research focuses on encoding speech properties such as loudness, tone, pitch, fluency, articulation, pauses, and context from arbitrary scales into vectorial data. We will then use machine learning models to conduct classification using past clinical trials as a source of training data. Finally, we will maximize model accuracy and efficiency to correctly classify unique voice data, to predict the likelihood of various neurodegenerative diseases. Our pilot research will focus on stress, mood, depression, and dementia with the hope of extending our research to more neurodegenerative diseases in the future as more clinical trials become accessible.
In the long term, we envision an application of this research launched into a usable, personalized, scalable API. The voice biomarker software will be launched alongside an interactive software featuring a talking voice bot, such that people can talk to a virtual companion, freely and in an unstructured environment, while allowing for their data to be screened. This allows for an easy-to-use program remotely at home, without the need for a hospital environment. The talking virtual companion is a feature of our API that is already working; it can currently conduct natural, interactive, and entertaining conversations while remembering the user’s previous dialogue, allowing it to track memory progress and its respective degradation.
With this research, a future of digital and advanced early detection screening is possible, allowing for decentralized, affordable, and accessible healthcare. With the new state-of-the-art possibilities of biomarkers, the general ambition is to detect disease up to 10 years before its onset. Such research has the power to transform mental health treatment. According to the UN, up to 1 billion people, nearly one in six of the world’s population suffer from neurological disorders, from Alzheimer’s and Parkinson’s diseases to strokes, multiple sclerosis, and infections. The large cognitive decline of the population is costing national healthcare billions in budget, for an increasingly costly healthcare system to the individual American. With the power to predict, detect, and manage certain neurological illnesses, we have the power to change millions of lives.
The goal of the project is to conduct research on voice biomarkers; quantifying speed, inflection, intonation, mood, memory, etc. to make precise estimates about mental health, namely the likelihood of neurodegeneration, and progress of mental well-being. The central aim of the research is to conduct research into the plausibility of using voice biomarkers as screening methods and early prevention mechanisms for Alzheimer’s, Parkinson’s, Depression, PTSD, and more. The first aim is to understand the correlations between voice and the development of neurodegenerative disease through computational methods. The next step is to generalize patterns in the population to gain a high accuracy in predicting disease. Finally, we want to create an accessible model that allows any person to use the technology through mobile software.
The funding is necessary for multiple steps in the research project:
Computational cost of machine learning model and LLM
Acquisition of voice data through hospitals, clinical trials and alternatively creating own databases of voice recordings
Funding of a team of 6 people
My name is Amelia Lubelska. I am pursuing an undergraduate degree in Molecular and Cellular Biology with a Specialization in Neurobiology. I am simultaneously pursuing Data Science and a certificate in Entrepreneurship from the Sutarja Engineering and Entrepreneurship Center at Berkeley. I am passionate about the intersection of neuroscience research, healthcare solutions, and personalized therapy.
I am supported by a team of 6 people and 2 professors who are all experts in machine learning, AI, human experimental research, and more! We are further supported by the Sutarja Engineering and Entrepreneurship Center at Berkeley and the Bakar Bioingenuity Hub at Berkeley.