Machine Learning and Signal Processing for Assistive Hearing and Speaking Devices
With the rapid advancement in speech processing technologies and in-depth understanding of human speech perception mechanism, significant improvement has been made in the design of assistive hearing devices [assistive listening device (ALD), hearing aids (HAs), and cochlear implants (CIs)] to benefit the speech communication for millions of hearing-impaired patients and subsequently enhance their quality of life. However, there are still many technical challenges, such as designing noise-suppression algorithms catered for ALD, HA, and CI users, deriving optimal compression strategies, improving the music appreciation, optimizing speech processing strategies for users speaking tonal languages, to name a few. In this talk, we present our recent research achievements using machine learning and signal processing on improving speech perception abilities for ALD, HA, and CI users.
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- Dr. Yu Tsao received the Ph.D. degree in Electrical and Computer Engineering from Georgia Institute of Technology, GA, USA, in 2008. The topic of his Ph.D. research is on characterizing unknown environments for enhancing automatic speech recognition (ASR) robustness under adverse conditions. In the summers of 2004, 2005, and 2006, Dr. Tsao was with Speech Technologies Laboratory, Texas Instruments Incorporated (TI), as a summer research associate, where he was deriving algorithms to reduce online computation for ASR on mobile devices. A US patent was applied based on his research work in TI. In addition to the patent application, Dr. Tsao received a TILU (Texas Instruments Leadership University) fellowship and two conference grants offered by ISCA (International Speech Communication Association). From April 2009 to September 2011, Dr. Tsao was an expert researcher at Spoken Language Communication (SLC) Group, National Institute of Information and Communications Technology (NICT), Kyoto, Japan, where he engaged in research and product development in ASR for multi-lingual speech-to-speech translation. Several papers were published, and a Japan Patent was filed based on his research achievements. Currently, Dr. Tsao is an associate research fellow of the Research Center for Information Technology Innovation (CITI) at Academia Sinica. His recent research interests include feature compensation and model adaptation for robust speech recognition, speaker and language recognition, audio event detection, speech enhancement, voice conversion, and text to speech.