Professor Jon Barker

About

Professor Jon Barker is a member of the Speech and Hearing Research Group. He has a first degree in Electrical and Information Sciences from Cambridge University, UK. After receiving a PhD from the University of Sheffield in 1999, he worked for some time at GIPSA-lab, Grenoble and IDIAP research institute in Switzerland before returning to Sheffield where he has had a permanent post since 2002.His research interests lie in noise-robust speech processing. Key application areas include distant-microphone speech recognition, speech intelligibility prediction and improved speech processing for hearing-aid users.

Selected publications

• Graetzer S, Akeroyd MA, Barker J, Cox TJ, Culling JF, Firth J, Naylor G, Porter E & Munoz RV (2027) The first Clarity Enhancement Challenge: Developing hearing aid algorithms for speech-in-noise. Computer Speech & Language, 102, 102021-102021.
• Roa-Dabike G, Cox TJ, Barker JP, Fazenda BM, Graetzer S, Vos RR, Akeroyd MA, Firth J, Whitmer WM, Bannister S & Greasley A (2026) The Cadenza lyric intelligibility prediction (CLIP) dataset. Data in Brief, 65, 112466-112466.
• Bannister S, Firth J, Roa-Dabike G, Vos R, Whitmer W, Greasley AE, Graetzer S, Fazenda B, Cox T, Barker J & Akeroyd MA (2026) The First Cadenza Challenge: Perceptual Evaluation of Machine Learning Systems to Improve Audio Quality of Popular Music for Those with Hearing Loss. Trends in Hearing, 30.
• Yue Z, Loweimi E, Cvetkovic Z, Barker J & Christensen H (2026) Raw acoustic-articulatory multimodal dysarthric speech recognition. Computer Speech & Language, 95, 101839-101839.
• SUTHERLAND R, CLARKE J, ELGHAZALY H, KUEBERT T, LUGGER M, PETRAUSCH S, ORTIZ JA, XU B, GOETZE S & BARKER JON (2025) Descriptor: Enhancing Conversations for the Hearing Impaired in the 9th Computational Hearing in Multisource Environments Challenge (CHiME9 ECHI). IEEE Data Descriptions, 1-9.
• Roa-Dabike G, Akeroyd MA, Bannister S, Barker JP, Cox TJ, Fazenda B, Firth J, Graetzer S, Greasley A, Vos RR & Whitmer WM (2025) The First Cadenza Challenges: Using Machine Learning Competitions to Improve Music for Listeners With a Hearing Loss. IEEE Open Journal of Signal Processing, 6, 722-734.
• Roa G, Bannister S, Firth JL, Graetzer S, Vos R, Akeroyd MA, Barker JP, Cox TJ, Fazenda B, Greasley A & Whitmer WM (2025) The second Cadenza machine learning challenge (CAD2): Improving music for people with hearing loss. The Journal of the Acoustical Society of America, 157(4_Supplement), A321-A321.
• Leglaive S, Fraticelli M, ElGhazaly H, Borne L, Sadeghi M, Wisdom S, Pariente M, Hershey JR, Pressnitzer D & Barker JP (2025) Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge. Computer Speech & Language, 89, 101685-101685.
• Roa Dabike G, Cox TJ, Miller AJ, Fazenda BM, Graetzer S, Vos RR, Akeroyd MA, Firth J, Whitmer WM, Bannister S , Greasley A et al (2024) The cadenza woodwind dataset: Synthesised quartets for music information retrieval and machine learning. Data in Brief, 57, 111199-111199.
• Whitmer WM, McShefferty D, Akeroyd MA, Bannister S, Barker JP, Cox TJ, Roa G, Fazenda B, Firth JL, Graetzer S , Greasley A et al (2024) Lyric intelligibility of musical segments for older individuals with hearing loss. The Journal of the Acoustical Society of America, 156(4_Supplement), A121-A121.