In this episode, Teri welcomes Jim Schwoebel, the founder and CEO of Neurolex, a diagnostics company that is applying speech analysis to detect various health conditions early, before full-blown symptoms occur. Their core vision is to pioneer a universal voice test, like a blood test with extracted features and reference ranges, for use in primary care to refer patients to specialists faster.
Jim is a Georgia Tech-trained biomedical engineer and co-founder/partner in Atlanta-based accelerator CyberLaunch. He got the idea for Neurolex after seeing his brother hospitalized for a psychotic episode, eventually being diagnosed with schizophrenia. He wondered how these types of conditions could be diagnosed earlier on, before the patient was past the point of pre-clinical intervention.
The company is currently in the midst of over 20 research trials taking place around the world, helped by 30 fellows they have recruited to help gather a massive dataset on voice diagnosis.
Key points from Jim!
- Voice signals and how they can be correlated with various types of illnesses and diseases.
- How patterns in voice could potentially help clinicians in diagnosing mental illness.
Research Findings
- You can predict with very high accuracy just with a voice sample who would or would not develop a psychotic episode.
Voice Samples
- Collecting samples is more of an in-clinic procedure because there are a lot of issues taking samples at home. It’s usually a short test that is done like a voice survey. It takes 3 to 5 minutes.
- For some diseases, there are alternative voice sample collection tests.
- The voice responses are sent to the cloud or locally in the clinic then a report is generated so that the health provider can use that information to infer the health of the patience.
- Jim believes with time they will find more robust models that can be used within home environments.
Audio Data Modeling
- They apply techniques that are use MFCC coefficients and ASR models to voice labels.
- For small data sets, they use old school techniques like support vector machine modeling or logistic regression.
- They either look at it as a binary problem or regression problem and estimate the scale itself, question by question from a voice file.
- They are continuously learning new features and traits that are correlated with voice features
- They also transcribe the audio and extract features from the text.
- Getting enough data is the biggest challenge right now
Voice Sample Sources
- They get them from academic collaborations. They have collaborations with the University of Washington where undergraduates go into clinics and collect data from patients. Patients have to consent to giving a voice sample.
- They’ve created a product called SurveyLex that helps them create, design and deploy voice surveys in the cloud like a SurveyMonkey survey. They have optimized it for research use and it gets a lot of data quickly. Different health entities are using the product on a subscription basis.
The Voice Genome Project
- They’ve been brainstorming on how to engage external collaborators in a more comprehensive way and also centralize their work because so far it’s too scattered. They have separate work a Harvard, MIT, Stanford, and UCSF.
- They are trying to create one survey using SurveyLex. They will launch it in January 2019.
- The first step will be getting a lot of survey information tied to voice information which will mainly include self-reported health inventories labeled with voice files.
- To contribute, people can donate their voice and be part of the research study or become a research collaborator.
- Collaborators can analyze the data beyond how Neurolex has.
Meaning of Voice First Health to Jim
- It’s looking within voice and using that information to improve healthcare through our work.
Links and Resources in this Episode
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