Harnessing speech signals to develop accurate and robust decision support tools: applications in biomedicine and the life sciences

Biomedical speech signal analysis has been gaining increasing momentum in the last 10-15 years. In this talk, I will focus on the key principles and state of the art signal processing algorithms to characterize sustained vowels towards obtaining clinically useful information. I will demonstrate how the extracted characteristics from speech signals can be combined with statistical machine learning techniques to develop accurate, robust, automated decision support tools assisting experts on their day-to-day praxis in the context of medical applications and forensic applications. I will highlight contemporary challenges and areas for further development, in particular with large speech databases that we have recently reported on such as the Parkinson’s Voice Initiative where we have collected 19,000+ samples across seven countries in acoustically uncontrolled settings.