Dr Tobias Goehring

Interests

My focus lies on improving the perception of speech for people with hearing loss in everyday life, especially in difficult listening situations with interfering background sounds. I combine techniques from Engineering, Auditory Neuroscience and Machine Learning to improve the performance of Medical Hearing Devices such as Cochlear implants and Hearing aids. My research spans the whole circle from the development of novel signal processing strategies and algorithms to the evaluation based on listening experiments with the target audience.

Research Focus

Keywords

Auditory Neuroscience

Cochlear implants

Speech perception

Machine Learning

Neural Networks

Clinical conditions

Deafness

Hearing and balance deficits

Equipment

Behavioural analysis

Computational modelling

Electrophysiological recording techniques

Field potential recording

Neuropsychological testing

Randomised control trials

Collaborators

Cambridge

Alan Archer-Boyd

Manohar Bance

Bob Carlyon

Brian Moore

Richard Turner

United Kingdom

Mark Fletcher Web: https://www.electrohaptics.co.uk/

International

Jessica Monaghan Web: https://researchers.mq.edu.au/en/pers...

Associated News Items


    Key publications

    Goehring T, Bolner F, Monaghan JJ, van Dijk B, Zarowski A, Bleeck S (2017), “Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users” Hearing research 344: 183-194

    Publications

    2019

    Fletcher MD, Hadeedi A, Goehring T, Mills SR (2019), “Electro-haptic enhancement of speech-in-noise performance in cochlear implant users” Scientific Reports 9 (1), 1-8

    Goehring T, Archer-Boyd A, Deeks JM, Arenberg JG, Carlyon RP (2019), “A Site-Selection Strategy based on Polarity Sensitivity for Cochlear Implants: Effects on Spectro-Temporal Resolution and Speech Perception” Journal of the Association for Research in Otolaryngology 1-18

    Goehring T, Keshavarzi M, Carlyon RP, Moore BCJ (2019), “Using recurrent neural networks to improve the perception of speech in non-stationary noise by people with cochlear implants” The Journal of the Acoustical Society of America 146 (1), 705-718

    Keshavarzi M, Goehring T, Turner RE, Moore BCJ (2019), “Comparison of effects on subjective intelligibility and quality of speech in babble for two algorithms: A deep recurrent neural network and spectral subtraction” The Journal of the Acoustical Society of America 145(3): 1493-1503

    2018

    Fletcher MD, Mills SR, Goehring T (2018), “Vibro-Tactile Enhancement of Speech Intelligibility in Multi-talker Noise for Simulated Cochlear Implant Listening” Trends in hearing 22: 2331216518797838

    Goehring T, Chapman JL, Bleeck S, Monaghan JJM (2018), “Tolerable delay for speech production and perception: effects of hearing ability and experience with hearing aids” International Journal of Audiology 57(1): 61-68

    Keshavarzi M, Goehring T, Zakis J, Turner RE, Moore BCJ (2018), “Use of a deep recurrent neural network to reduce wind noise: Effects on judged speech intelligibility and sound quality” Trends in hearing 22: 2331216518770964

    2017

    Monaghan JJ, Goehring T, Yang X, Bolner F, Wang S, Wright MC, Bleeck S (2017), “Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners” The Journal of the Acoustical Society of America 141(3): 1985-1998

    2016

    Bolner F, Goehring T, Monaghan JJM, Van Dijk B, Wouters J, Bleeck S (2016), “Speech enhancement based on neural networks applied to cochlear implant coding strategies” IEEE ICASSP conference 2016, 6520-6524