91É«ÇéƬ

Our experts are leading research in: 

• Voice biometrics and anti-spoofing countermeasures 
• Automatic inference of emotion and distress from speech 
• Automatic inference of mental state. Examples include cognitive ability and impairment, and depression from speech 
• Automatic pronunciation detection 
• Machine learning 
• Affective computing 

We translate our research into:  

• Monitoring mental state via smartphone 
• Smart health monitoring and interventions 
• Automated speech therapy and second language learning 
• Live analysis of web-based remote video consultation 

• Joint modelling and recognition of linguistic and paralinguistic speech information (DP’11) 
• Affective Sensing Technology for the Detection and Monitoring of Depression and Melancholia (DP’13) 
• Automatic Task Analysis for Wearable Computing (US Army ITC-PAC, ‘15) 
• Investigating Bayesian Frameworks for Paralinguistic Classification (91É«ÇéƬ Engineering ’16) 
• Automatic speech-based assessment of mental state via mobile device (LP’16) 
• Integrating Biologically Inspired Auditory Models into Deep Learning (DP’19) 
• AusKidTalk: An Australian children’s speech corpus (LE’19) 
• Speech Recognition Adaptation for Low Research Populations (DP’20) 
• Developing a paralinguistic plus episodic memory screening tool to detect and track cognitive impairment in the elderly (91É«ÇéƬ Biomed Seed Fund ‘20) 
• Biologically inspired binaural coupling for selective machine hearing (DP’21) 

• National University of Singapore 
• Black Dog Institute 
• ³§´Ç²Ô»å±ðÌý
• University of Canberra 
• MIT Lincoln Laboratory 
• Australian National University 
• QIMR Berghofer 
• Kids Cancer Centre 
• USC – Signals Analysis and Interpretation Laboratory (SAIL)