Abstract

 

Cochlear implants (CIs) are surgically implanted electronic devices that provide a sense of sound in patients with profound to severe hearing loss. The considerable progress of CI technologies in the past three decades has enabled many CI users to enjoy a high level of speech understanding in quiet. For most CI users, however, understanding speech in noisy environments remains a challenge. In this talk, I will present the deep learning based noise reduction (NR) approach, which has been demonstrated its effectiveness for improving the speech intelligibility for CI recipients. Experimental results indicated that deep learning based NR yields higher intelligibility scores than conventional approaches for Mandarin-speaking listeners, suggesting that DDAE NR could potentially be integrated into a CI processor to overcome speech perception degradation caused by noise. 

 

Bio:Dr. Ying-Hui Lai received the Ph.D. degree in department of biomedical engineering from National Yang-Ming University in 2013. From January 2010 to June 2012, Dr. Lai was as a research and development (R&D) engineering in Aescu technology, Taipei, Taiwan, where he engaged in research and product development in hearing aids. From October 2013 to September 2016, Dr. Lai was a postdoctoral fellow of the research center for information technology innovation at Academia Sinica. Following, Dr. Lai was in the department of electronic engineering, Yuan Ze university as assistant Professor for one year. Currently, Dr. Lai is an assistant Professor of department of biomedical engineering at National Yang-Ming University. His research interests include hearing aids, cochlear implant, speech enhancement, voice conversion, audio event detection and deep learning.