Dr Tobias Goehring

University position

MRC Fellow (Senior Research Associate)

Dr Tobias Goehring is pleased to consider applications from prospective PhD students.


Department of Medicine


MRC Cognition and Brain Sciences Unit

Home page


Research Themes

Cognitive and Behavioural Neuroscience

Clinical and Veterinary Neuroscience


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. I hold a 5-year Fellowship (Career Development Award) from the Medical Research Council (UK Research and Innovation) and lead the Deep Hearing Lab at the MRC CBU (www.deephearinglab.com).

Research Focus


Auditory Neuroscience

Cochlear implants

Speech perception

Machine Learning

Neural Networks

Clinical conditions


Hearing and balance deficits


Behavioural analysis

Computational modelling

Electroencephalography (EEG)

Electrophysiological recording techniques

Neuropsychological testing

Randomised control trials



Manohar Bance

Bob Carlyon

Matt Davis

Brian Moore

Debi Vickers

United Kingdom

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


Julie Arenberg Web: https://oto.hms.harvard.edu/people/...

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

Associated News Items

    Key publications

    Carlyon RP, Goehring T (2021), “Cochlear Implant Research and Development in the Twenty-first Century: A Critical Update” Journal of the Association for Research in Otolaryngology 22(5): 481-508

    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



    Brochier T, Schlittenlacher J, Roberts I, Goehring T, Jiang C, Vickers D, Bance M (2022), “From Microphone to Phoneme: An End-to-End Computational Neural Model for Predicting Speech Perception with Cochlear Implants” IEEE Trans on Biomed Eng

    Goehring T, Monaghan J (2022), “Helping People Hear Better With “Smart” Hearing Devices” Frontiers for Young Minds


    Garcia C, Goehring T, Cosentino S, Turner RE, Deeks JM, Brochier T, Rughooputh T, Bance M, Carlyon RP (2021), “The panoramic ECAP method: estimating patient-specific patterns of current spread and neural health in cochlear implant users” Journal of the Association for Research in Otolaryngology 1-23

    Goehring T, Archer-Boyd AW, Arenberg JG, Carlyon RP (2021), “The effect of increased channel interaction on speech perception with cochlear implants” Scientific Reports 11 (1), 1-9

    Jiang C, Singhal S, Landry T, Roberts IV, de Rijk SR, Brochier T, Goehring T, Tam YC, Carlyon RP, Malliaras GM, Bance ML (2021), “An Instrumented Cochlea Model for the Evaluation of Cochlear Implant Electrical Stimulus Spread” IEEE Trans Biomed Eng


    Archer-Boyd A, Goehring T, Carlyon B (2020), “The effect of free-field presentation and processing strategy on a measure of spectro-temporal processing by cochlear-implant listeners” PsyArXiv

    Goehring T, Arenberg J, Carlyon B (2020), “Using spectral blurring to assess effects of channel interaction on speech-in-noise perception with cochlear implants” PsyArXiv

    Lamping W, Goehring T, Marozeau J, Carlyon B (2020), “The effect of a coding strategy that removes temporally masked pulses on speech perception by cochlear implant users” PsyArXiv


    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


    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


    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


    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

    Goehring T, Yang X, Monaghan J, Bleeck S (2016), “Speech enhancement for hearing-impaired listeners using deep neural networks with auditory-model based features” IEEE EUSIPCO conference 2016