Publications

Journal Papers

  1. Bannister, S., Greasley, A. E., Cox, T. J., Akeroyd, M. A., Barker, J., Fazenda, B., … Whitmer, W. M. (2024). Muddy, muddled, or muffled? Understanding the perception of audio quality in music by hearing aid users. Frontiers in Psychology, 15. 10.3389/fpsyg.2024.1310176
  2. Firth, J. L., Cox, T. J., Greasley, A., Barker, J. P., Whitmer, W. M., Fazenda, B., … Akeroyd, M. A. (2023). A systematic review of measurements of real-world interior car noise for the “Cadenza” machine-learning project. Journal of the Acoustical Society of America, 153, A332–A332.
  3. Akeroyd, M. A., Firth, J. L., Naylor, G., Barker, J. P., Culling, J., Cox, T. J., … Griffiths, H. (2023). Results of the second “clarity” enhancement challenge for hearing devices. Journal of the Acoustical Society of America, 153, A48–A48.
  4. Yue, Z., Loweimi, E., Christensen, H., Barker, J., & Cvetkovic, Z. (2022). Acoustic modelling from raw source and filter components for dysarthric speech recognition. IEEE Transactions on Audio, Speech and Language Processing, 30, 2968–2980. 10.1109/taslp.2022.3205766
  5. Graetzer, S., Akeroyd, M. A., Barker, J., Cox, T. J., Culling, J. F., Naylor, G., … Viveros-Muñoz, R. (2022). Dataset of British English speech recordings for psychoacoustics and speech processing research: The clarity speech corpus. Data in Brief, 41(107951), 2711. 10.1016/j.dib.2022.107951
  6. Akeroyd, M. A., Barker, J. P., Cox, T. J., Culling, J., Graetzer, S., Naylor, G., … Viveros Muñoz, R. (2021). Launching the first “Clarity” Machine Learning Challenge to revolutionise hearing device processing. Journal of the Acoustical Society of America, 148(4), 2711. 10.1121/1.5147514
  7. Cooke, M., Lecumberri, M. L. G., Barker, J., & Marxer, R. (2019). Lexical frequency effects in English and Spanish word misperceptions. Journal of the Acoustical Society of America, 145(2), EL136–EL141. 10.1121/1.5090196 [PDF]
  8. Alghamdia, N., Maddock, S., Marxerb, R., Barker, J., & Brown, G. J. (2018). A corpus of audio-visual Lombard speech with frontal and profile views. Journal of the Acoustical Society of America, 143(6), EL523–EL529. 10.1121/1.5042758 [PDF]
  9. Marxer, R., Barker, J., Alghamdi, N., & Maddock, S. (2018). The impact of the Lombard effect on audio and visual speech recognition systems. Speech Communication, 100, 58–68. 10.1016/j.specom.2018.04.006 [PDF]
  10. Gonzalez, J. A., Gómez, A. M., Peinado, A. M., Ma, N., & Barker, J. (2017). Spectral Reconstruction and Noise Model Estimation Based on a Masking Model for Noise Robust Speech Recognition. Circuits, Systems, and Signal Processing, 36(9), 3731–3760. 10.1007/s00034-016-0480-7 [PDF]
  11. Alghamdi, N., Maddock, S., Barker, J., & Brown, G. J. (2017). The impact of automatic exaggeration of the visual articulatory features of a talker on the intelligibility of spectrally distorted speech. Speech Communication, 95, 127–136. 10.1016/j.specom.2017.08.010 [PDF]
  12. Vincent, E., Watanabe, S., Nugraha, A. A., Barker, J., & Marxer, R. (2017). An analysis of environment, microphone and data simulation mismatches in robust speech recognition. Computer Speech and Language, 46, 535–557. 10.1016/j.csl.2016.11.005 [PDF]
  13. Barker, J., Marxer, R., Vincent, E., & Watanabe, S. (2017). The third ‘CHiME’ speech separation and recognition challenge: Analysis and outcomes. Computer Speech and Language, 46, 605–626. 10.1016/j.csl.2016.10.005 [PDF]
  14. Marxer, R., Barker, J., Cooke, M., & Garcia Lecumberri, M. L. (2016). A corpus of noise-induced word misperceptions for English. Journal of the Acoustical Society of America, 140(5), EL458–EL463. Retrieved from http://eprints.whiterose.ac.uk/108991/ [PDF]
  15. Gonzalez, J., Peinado, A., Ma, N., Gomez, A., & Barker, J. (2013). MMSE-based missing-feature reconstruction with temporal modeling for robust speech recognition. IEEE Transactions on Audio, Speech and Language Processing, 21(3), 624–635. 10.1109/TASL.2012.2229982 [PDF]
  16. Barker, J., Vincent, E., Ma, N., Christensen, H., & Green, P. (2013). The PASCAL CHiME Speech Separation and Recognition Challenge. Computer Speech and Language, 27(3), 621–633. 10.1016/j.csl.2012.10.004 [PDF]
  17. Carmona, J. L., Barker, J., Gomez, A. M., & Ma, N. (2013). Speech spectral envelope enhancement by HMM-based analysis/resynthesis. IEEE Signal Processing Letters, 20(6), 563–566. 10.1109/LSP.2013.2255125
  18. Ma, N., Barker, J., Christensen, H., & P., G. (2013). A hearing-inspired approach for distant-microphone speech recognition in the presence of multiple sources. Computer Speech and Language, 27(3), 820–836. 10.1016/j.csl.2012.09.001 [PDF]
  19. N. Ma, J. Barker, H. Christensen, & P. Green. (2012). Combining speech fragment decoding and adaptive noise floor modelling. IEEE Transactions on Audio, Speech and Language Processing, 20(3), 818–827.
  20. J. Barker, N. Ma, A. Coy, & M. Cooke. (2010). Speech fragment decoding techniques for simultaneous speaker identification and speech recognition. Computer Speech and Language, 24(1), 94–111. 10.1016/j.csl.2008.05.003 [PDF]
  21. Barker, J., & Shao, X. (2009). Energetic and informational masking effects in an audio-visual speech recognition system. IEEE Transactions on Audio, Speech and Language Processing, 17(3), 446–458. 10.1109/TASL.2008.2011534 [PDF]
  22. X. Shao, & J. P. Barker. (2008). Stream weight estimation for multistream audio-visual speech recognition in a multispeaker environment. Speech Communication, 50(4), 337–353. 10.1016/j.specom.2007.11.002 [PDF]
  23. Christensen, H., Ma, N., Wrigley, S. N., & Barker, J. (2008). Improving source localisation in multi-source, reverberant conditions: exploiting local spectro-temporal location cues. Journal of the Acoustical Society of America, 123(5), 3294. 10.1121/1.2933688
  24. Cooke, M., Garcia Lecumberri, M. L., & Barker, J. P. (2008). The foreign language cocktail party problem: Energetic and informational masking effects in non-native speech perception. Journal of the Acoustical Society of America, 123(1), 414–427. doi:10.1121/1.2804952 [PDF]
  25. N. Ma, P. Green, J. Barker, & A. Coy. (2007). Exploiting correlogram structure for robust speech recognition with multiple speech sources. Speech Communication, 49(12), 874–891. 10.1016/j.specom.2007.05.003 [PDF]
  26. A. Coy, & J. Barker. (2007). An automatic speech recognition system based on the scene analysis account of auditory perception. Speech Communication, 49(7), 384–401. 10.1016/j.specom.2006.11.002 [PDF]
  27. J. Barker, & M. Cooke. (2007). Modelling speaker intelligibility in noise. Speech Communication, 49(5), 402–417. 10.1016/j.specom.2006.11.003 [PDF]
  28. S. Harding, J. Barker, & G. J. Brown. (2006). Mask estimation for missing data speech recognition based on statistics of binaural interaction. IEEE Trans. Speech and Audio Processing. IEEE Transactions on Audio, Speech and Language Processing, 14(1), 58–67. 10.1109/TSA.2005.860354 [PDF]
  29. M. Cooke, J. Barker, S. Cunningham, & X. Shao. (2006). An audio-visual corpus for speech perception and automatic speech recognition. Journal of the Acoustical Society of America, 120(5), 2421–2424. 10.1121/1.2229005 [PDF]
  30. J. Barker, M. P. Cooke, & D. P. W. Ellis. (2005). Decoding speech in the presence of other sources. Speech Communication, 45(1), 5–25. doi:10.1016/j.specom.2004.05.002 [PDF]
  31. K. J. Palomäki, G. J. Brown, & J. Barker. (2004). Techniques for handling convolutional distortion with ‘missing data’ automatic speech recognition. Speech Communication, 43(1–2), 123–142. 10.1016/j.specom.2004.02.005 [PDF]
  32. Barker, J. P., & Cooke, M. P. (1999). Is the sine-wave speech cocktail party worth attending? Speech Communication, 27(3–4), 159–174. 10.1016/S0167-6393(98)00081-8 [PDF]
  33. Barker, J. P., & Cooke, M. P. (1996). Modeling the recognition of sine-wave sentences. Journal of the Acoustical Society of America, 100(4), 2682. 10.1121/1.416995

Conference Papers

  1. Mogridge, R., Close, G., Sutherland, R., Hain, T., Barker, J., Goetze, S., & Ragni, A. (2024). Non-Intrusive Speech Intelligibility Prediction for Hearing-Impaired Users using Intermediate ASR Features and Human Memory Models. In Proceedings of the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Seoul, Korea: IEEE.
  2. Barker, J., Akeroyd, M., Bailey, W., Cox, T., Culling, J., Firth, J., … Naylor, G. (2024). THE 2ND CLARITY PREDICTION CHALLENGE: A MACHINE LEARNING CHALLENGE FOR HEARING AID INTELLIGIBILITY PREDICTION. In Proceedings of the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Seoul, Korea: IEEE.
  3. Dabike, G. R., Akeroyd, M., Bannister, S., Barker, J., Cox, T., Firth, B. F. J., … Whitmer, W. (2024). The ICASSP SP Cadenza Challenge: Music Demixing/Remixing For Hearing Aids. In Proceedings of the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Seoul, Korea: IEEE.
  4. Cox, T., Akeroyd, M., Barker, J., Culling, J., Firth, J., Graetzer, S., … Porter, E. (2023). Predicting speech intelligibility for people with a hearing loss: The clarity challenges. In INTER-NOISE and NOISE-CON Congress and Conference Proceedings (pp. 4599–4606). Institute of Noise Control Engineering. 10.1109/ICASSP43922.2022.9746855
  5. Cox, T. J., Barker, J., Bailey, W., Graetzer, S., Akeroyd, M. A., Culling, J. F., & Naylor, G. (2023). Overview of the 2023 ICASSP SP Clarity Challenge: Speech Enhancement for Hearing Aids. In Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Rhodes Island, Greece: IEEE. https://doi.org/10.1109/icassp49357.2023.10433922
  6. Akeroyd, M., Bailey, W., Bannister, S., Firth, J., Graetzer, S., Dabike, G. R., … Vos, R. (2023). The Clarity & Cadenza Challenges. In Proceedings of Forum Acusticum (pp. 1209–1211). Torino, Italy. 10.61782/fa.2023.0876
  7. Akeroyd, M. A., Bailey, W., Barker, J., Cox, T. J., Culling, J. F., Graetzer, S., … Tu, Z. (2023). The 2nd Clarity Enhancement Challenge for Hearing Aid Speech Intelligibility Enhancement: Overview and Outcomes. In Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Rhodes Island, Greece: IEEE. 10.1109/ICASSP49357.2023.10094918
  8. Tu, Z., Ma, N., & Barker, J. (2022). Unsupervised Uncertainty Measures of Automatic Speech Recognition for Non-intrusive Speech Intelligibility Prediction. In Proceedings of the 23nd Annual Conference of the International Speech Communication Association (INTERSPEECH 2022). Incheon, Korea.
  9. Tu, Z., Deadman, J., Ma, N., & Barker, J. (2022). Auditory-Based Data Augmentation for end-to-end Automatic Speech Recognition. In Proceedings of the 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2022). Singapore: IEEE. 10.1109/ICASSP43922.2022.9746252
  10. Tu, Z., Ma, N., & Barker, J. (2022). Exploiting Hidden Representations from a DNN-based Speech Recogniser for Speech Intelligibility Prediction in Hearing-impaired Listeners. In Proceedings of the 23nd Annual Conference of the International Speech Communication Association (INTERSPEECH 2022). Incheon, Korea.
  11. Zhang, J., Zorila, C., Doddipatla, R., & Barker, J. (2022). On monoaural speech enhancement for automatic recognition of real noisy speech using mixture invariant training. In Proceedings of the 23nd Annual Conference of the International Speech Communication Association (INTERSPEECH 2022). Incheon, Korea.
  12. Deadman, J., & Barker, J. (2022). Improved Simulation of Realistically-Spatialised Simultaneous Speech Using Multi-Camera Analysis in The Chime-5 Dataset. In Proceedings of the 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2022). Singapore: IEEE. 10.1109/ICASSP43922.2022.9746351
  13. Yue, Z., Loweimi, E., Christensen, H., Barker, J., & Cvetkovic, Z. (2022). Dysarthric Speech Recognition From Raw Waveform with Parametric CNNs. In Proceedings of the 23nd Annual Conference of the International Speech Communication Association (INTERSPEECH 2022). Incheon, Korea.
  14. Yue, Z., Loweimi, E., Cvetkovic, Z., Christensen, H., & Barker, J. (2022). Multi-Modal Acoustic-Articulatory Feature Fusion For Dysarthric Speech Recognition. In Proceedings of the 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2022). Singapore: IEEE. 10.1109/ICASSP43922.2022.9746855
  15. Deadman, J., & Barker, J. (2022). Modelling Turn-taking in Multispeaker Parties for Realistic Data Simulation. In Proceedings of the 23nd Annual Conference of the International Speech Communication Association (INTERSPEECH 2022). Incheon and Korea.
  16. Barker, J., Akeroyd, M., Cox, T. J., Culling, J. F., Firth, J., Graetzer, S., … Munoz, R. V. (2022). The 1st Clarity Prediction Challenge: A machine learning challenge for hearing aid intelligibility prediction. In Proceedings of the 23nd Annual Conference of the International Speech Communication Association (INTERSPEECH 2022). Incheon, Korea.
  17. Roa Dabike, G., & Barker, J. (2021). The use of voice source features for sung speech recognition. In Proceedings of the 46th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2021). Toronto, Canada: IEEE. 10.1109/ICASSP39728.2021.9414950
  18. Zhang, J., Zorila, C., Doddipatla, R., & Barker, J. (2021). Teacher-student MixIT: semi-supervised learning for speech separatioN. In Proceedings of the 22nd Annual Conference of the International Speech Communication Association (INTERSPEECH 2021). Brno, Czech Republic.
  19. Zhang, J., Zorila, C., Doddipatla, R., & Barker, J. (2021). Time-domain speech extraction with spatial information and multi speaker conditioning mechanism. In Proceedings of the 46th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2021). Toronto, Canada: IEEE. 10.1109/ICASSP39728.2021.9414092
  20. Yue, Z., Barker, J., Christensen, H., McKean, C., Ashton, E., Wren, Y., … Brigh, R. (2021). Parental spoken scaffolding and narrative skills in crowd-sourced storytelling samples of young children. In Proceedings of the 22nd Annual Conference of the International Speech Communication Association (INTERSPEECH 2021). Brno, Czech Republic.
  21. Tu, Z., Ma, N., & Barker, J. (2021). Optimising hearing aid fittings for speech in noise with a differentiable hearing loss model. In Proceedings of the 22nd Annual Conference of the International Speech Communication Association (INTERSPEECH 2021). Brno, Czech Republic.
  22. Tu, Z., Ma, N., & Barker, J. (2021). DHASP: Differentiable hearing aid speech processing. In Proceedings of the 46th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2021). Toronto, Canada: IEEE. 10.1109/ICASSP39728.2021.9414571
  23. Graetzer, S., Barker, J., Cox, T., Akeroyd, M., Culling, J., Naylor, G., … Munoz, R. V. (2021). Clarity-2021 challenges: Datasets, tasks and baselines. In Proceedings of the 22nd Annual Conference of the International Speech Communication Association (INTERSPEECH 2021). Brno, Czech Republic.
  24. Falconer, L., Coy, A., & Barker, J. (2021). Modelling the Effects of Hearing Aid Algorithms on Speech and Speaker Intelligibility as Perceived by Listeners with Simulated Sensorineural Hearing Impairment. In Proceedings of IEEE SoutheastCon (pp. 1–8). virtual. 10.1109/SoutheastCon45413.2021.9401882
  25. Xiong, F., Barker, J., Yue, Z., & Christensen, H. (2020). Source domain data selection for improved transfer learning targeting dysarthric speech recognition. In Proceedings of the 45nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2020). Barcelona, Spain: IEEE. 10.1109/ICASSP40776.2020.9054694
  26. Deadman, J., & Barker, J. (2020). Simulating realistically-spatialised simultaneous speech using video-driven speaker detection and the CHiME-5 dataset. In Proceedings of the 21st Annual Conference of the International Speech Communication Association (INTERSPEECH 2020). Shanghai, China.
  27. Zhang, J., Zorila, C., Doddipatla, R., & Barker, J. (2020). On end-to-end multi-channel time domain speech separation in reverberant environments. In Proceedings of the 45nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2020). Barcelona, Spain: IEEE. 10.1109/ICASSP40776.2020.9053833
  28. Yue, Z., Christensen, H., & Barker, J. (2020). Autoencoder bottleneck features with multi-task optimisation for improved continuous dysarthric speech recognition. In Proceedings of the 21st Annual Conference of the International Speech Communication Association (INTERSPEECH 2020). Shanghai, China.
  29. Yue, Z., Xiong, F., Christensen, H., & Barker, J. (2020). Exploring appropriate acoustic and language modelling choices for continuous dysarthric speech recognition. In Proceedings of the 45nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2020). Barcelona, Spain: IEEE. 10.1109/ICASSP40776.2020.9054343
  30. Xiong, F., Barker, J., & Christensen, H. (2019). Phonetic Analysis of Dysarthric Speech Tempo and Applications to Robust Personalised Dysarthric Speech Recognition. In Proceedings of the 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2019). Brighton, UK: IEEE. 10.1109/ICASSP.2019.8683091
  31. Dabike, G. R., & Barker, J. (2019). Automatic lyric transcription from Karaoke vocal tracks: Resources and a Baseline System. In Proceedings of the 20th Annual Conference of the International Speech Communication Association (INTERSPEECH 2019). Graz, Austria.
  32. Loweimi, E., Barker, J., & Hain, T. (2018). On the Usefulness of the Speech Phase Spectrum for Pitch Extraction. In Proceedings of the 19th Annual Conference of the International Speech Communication Association (INTERSPEECH 2018). Hyderabad, India.
  33. Xiong, F., Zhang, J., Meyer, B., Christensen, H., & Barker, J. (2018). Channel selection from DNN posterior probability for speech recognition with distributed microphone arrays in everyday environments. In Proceedings of the 5th ISCA International Workshop on Speech Processing in Everyday Environments (CHiME 2018) (pp. 19–24). Hyderabad, India.
  34. Loweimi, E., Barker, J., & Hain, T. (2018). Exploring the use of group delay for generalised VTS based noise compensation. In Proceedings of the 43nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2018). Calgary, Canada: IEEE. 10.1109/ICASSP.2018.8462595
  35. Gogate, M., Adeel, A., Marxer, R., Barker, J., & Hussain, A. (2018). DNN driven Speaker Independent Audio-Visual Mask Estimation for Speech Separation. In Proceedings of the 19th Annual Conference of the International Speech Communication Association (INTERSPEECH 2018). Hyderabad, India.
  36. Barker, J., Watanabe, S., Vincent, E., & Trmal, J. (2018). The fifth ‘CHiME’ Speech Separation and Recognition Challenge: Dataset, task and baselines. In Proceedings of the 19th Annual Conference of the International Speech Communication Association (INTERSPEECH 2018). Hyderabad, India.
  37. Loweimi, E., Barker, J., & Hain, T. (2017). Channel Compensation in the Generalised Vector Taylor Series Approach to Robust ASR. In Proceedings of the 18th Annual Conference of the International Speech Communication Association (INTERSPEECH 2017). Stockholm, Sweden.
  38. Loweimi, E., Barker, J., & Hain, T. (2017). Statistical normalisation of phase-based feature representation for robust speech recognition. In Proceedings of the 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2017). New Orleans, USA: IEEE. 10.1109/ICASSP.2017.7953170
  39. Loweimi, E., Barker, J., Saz Torralba, O., & Hain, T. (2017). Robust Source-Filter Separation of Speech Signal in the Phase Domain. In Proceedings of the 18th Annual Conference of the International Speech Communication Association (INTERSPEECH 2017). Stockholm, Sweden.
  40. Marxer, R., & Barker, J. (2017). Binary Mask Estimation Strategies for Constrained Imputation-Based Speech Enhancement. In Proceedings of the 18th Annual Conference of the International Speech Communication Association (INTERSPEECH 2017). Stockholm, Sweden.
  41. Hussain, A., Barker, J., Marxer, R., Adeel, A., Whitmer, W., Watt, R., & Derleth, P. (2017). Towards Multi-modal Hearing Aid Design and Evaluation in Realistic Audio-Visual Settings: Challenges and Opportunities. In Proceedings of the 1st ISCA International Workshop on Challenges in Hearing Assistive Technology (CHAT-2017) (pp. 29–34). Stockholm, Sweden.
  42. Abel, A., Marxer, R., Barker, J., Watt, R., Whitmer, B., Derleth, P., & Hussain, A. (2016). A data driven approach to audiovisual speech mapping. In Proceedings of Advances in Brain Inspired Cognitive Systems: 8th International Conference, BICS 2016 (pp. 331–342). Beijing, China.
  43. Garcia Lecumberri, M. L., Barker, J., Marxer, R., & Cooke, M. (2016). Language effects in noise-induced word misperceptions. In Proceedings of the 17th Annual Conference of the International Speech Communication Association (INTERSPEECH 2016). San Francisco, CA.
  44. Loweimi, E., Barker, J., & Hain, T. (2016). Use of Generalised Nonlinearity in Vector Taylor Series Noise Compensation for Robust Speech Recognition. In Proceedings of the 17th Annual Conference of the International Speech Communication Association (INTERSPEECH 2016). San Francisco, CA.
  45. Mandel, M. I., & Barker, J. (2016). Multichannel Spatial Clustering for Robust Far-Field Automatic Speech Recognition in Mismatched Conditions. In Proceedings of the 17th Annual Conference of the International Speech Communication Association (INTERSPEECH 2016). San Francisco, CA.
  46. Tóth, A. M., Cooke, M., & Barker, J. (2016). Misperceptions arising from speech-in-babble interactions. In Proceedings of the 17th Annual Conference of the International Speech Communication Association (INTERSPEECH 2016). San Francisco, CA.
  47. Barker, J., Marxer, R., Vincent, E., & Watanabe, S. (2015). The third CHiME speech separation and recognition challenge: dataset, task and baselines. In Proc. 2015 IEEE Automatic Speech Recognition and Understanding (ASRU). Scottsdale, AZ.
  48. Marxer, R., Cooke, M., & Barker, J. (2015). A Framework for the Evaluation of Microscopic Intelligibility Models. In Proceedings of the 16th Annual Conference of the International Speech Communication Association (INTERSPEECH 2015). Dresden, Germany.
  49. Alghamdi, N., Maddock, S., Brown, G. J., & Barker, J. (2015). A Comparison of Audiovisual and Auditory-only Training on the Perception of Spectrally-distorted Speech. In Proc. XVIII International Congress of Phonetics Sciences (ICPhS) 2015. Glasgow, UK.
  50. Foster, P., Sigtia, S., Krstulovic, S., Barker, J., & D. Plumbley, M. (2015). CHiME-HOME A Dataset for Sound Source Recognition in a Domestic Environment. In Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA. New Paltz, NY.
  51. Lin, L., Barker, J., & J. Brown, G. (2015). The Effect of Cochlear Implant Processing on Speaker Intelligibility: A Perceptual Study and Computer Model. In Proceedings of the 16th Annual Conference of the International Speech Communication Association (INTERSPEECH 2015). Dresden, Germany.
  52. Loweimi, E., Barker, J., & Hain, T. (2015). Source-filter Separation of Speech Signal in the Phase Domain. In Proceedings of the 16th Annual Conference of the International Speech Communication Association (INTERSPEECH 2015). Dresden, Germany.
  53. E. Loweimi, J. B., M. Doulaty, & Hain, T. (2015). Long-term Statistical Feature Extraction from Speech Signals and its Application in Emotion Recognition. In Proc. 3rd International Conference on Statistical Language and Speech Processing (SLSP). Budapest, Hungary.
  54. Alghamdi, N., Maddock, S., Brown, G. J., & Barker, J. (2015). Investigating the Impact of Aritificial Enhancement of Lip Visibility on the Intelligibility of Spectrally-Distorted Speech. In Proc. The 1st Joint Conference on Facial Analysis, Animation and Auditory-Visual Speech Processing, FAAVSP2015. Vienna, Austria.
  55. Dabel, M. A., & Barker, J. (2015). On the Role of Discriminative Intelligibility Models for Speech Intelligibility Enhancement. In Proc. XVIII International Congress of Phonetics Sciences (ICPhS) 2015. Glasgow, UK.
  56. Ma, N., Marxer, R., J.Barker, & Brown, G. J. (2015). Exploiting synchrony spectra and deep neural networks for noise-robust automatic speech recognition. In Proc. 2015 IEEE Automatic Speech Recognition and Understanding (ASRU). Scottsdale, AZ.
  57. Dabel, M. A., & Barker, J. (2014). Speech pre-enhancement using a discriminative microscopic intelligibility model. In Proceedings of the 15th Annual Conference of the International Speech Communication Association (INTERSPEECH 2014). Singapore.
  58. Vincent, E., Barker, J., Watanabe, S., Le Roux, J., Nesta, F., & Matassoni, M. (2013). The second ‘CHiME’ Speech Separation and Recognition Challenge: Datasets, tasks and baselines. In Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Vancouver, Canada: IEEE.
  59. N. Ma, & J. Barker. (2013). A fragment-decoding plus missing-data imputation system evaluated on the 2nd CHiME challenge. In Proceedings of the 2nd CHiME Workshop on Machine Listening in Multisource Environments (pp. 53–58). Vancouver, Canada. [PDF]
  60. Ma, N., & Barker, J. (2012). Coupling identification and reconstruction of missing features for noise-robust automatic speech recognition. In Proceedings of the 13th Annual Conference of the International Speech Communication Association (Interspeech 2012). Portland, Oregon.
  61. González, J. A., Peinado, A. M., Gómez, A. M., Ma, N., & Barker, J. (2012). Combining missing-data reconstruction and uncertainty decoding for robust speech recognition. In Proceedings of the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4693–4696). Kyoto, Japan: IEEE. 10.1109/ICASSP.2012.6288966 [PDF]
  62. Cooke, M., Barker, J., Lecumberri, M. L., & Wasilewski, K. (2011). Crowdsourcing for word recognition in noise. In Proceedings of the 12th Annual Conference of the International Speech Communication Association (Interspeech 2011). Florence.
  63. N. Ma, J. Barker, H. Christensen, & P. Green. (2011). Incorporating localisation cues in a fragment decoding framework for distant binaural speech recognition. In IEEE Joint Workshop on Hands-Free Speech Communication and Microphone Arrays (HSCMA’11) (pp. 207–212). Edinburgh, United Kingdom. 10.1109/HSCMA.2011.5942400
  64. N. Ma, J. Barker, H. Christensen, & P. Green. (2011). Recent advances in fragment-based speech recognition in reverberant multisource environments. In Proceedings of the 1st CHiME Workshop on Machine Listening in Multisource Environments (pp. 68–73). Florence, Italy.
  65. Morales-Cordovilla, J. A., Ma, N., Sánchez, V., Carmona, J. L., Peinado, A. M., & Barker, J. (2011). A pitch based noise estimation technique for robust speech recognition with missing data. In Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4808–4811). Prague, Czech Republic: IEEE. 10.1109/ICASSP.2011.5947431 [PDF]
  66. N. Ma, J. Barker, H. Christensen, & P. Green. (2011). Binaural cues for fragment-based speech recognition in reverberant multisource environments. In Proceedings of the 12th Annual Conference of the International Speech Communication Association (Interspeech 2011) (pp. 1657–1660). Florence, Italy. [PDF]
  67. H. Christensen, J. Barker, N. Ma, & P. Green. (2010). The CHiME corpus: a resource and a challenge for Computational Hearing in Multisoure Environments. In Proceedings of the 11th Annual Conference of the International Speech Communication Association (Interspeech 2010). Makuhari, Japan.
  68. Christensen, H., & Barker, J. (2010). Speaker turn tracking with mobile microphones: combining location and pitch information. In Proceedings of the 18th European Signal Processing Conference (EUSIPCO-2010). Aalborg, Denmark.
  69. Kabir, A., Giurgiu, M., & Barker, J. (2010). Robust automatic transcription of English speech corpora. In Proceedings of the 8th International Conference on Communications (COMM) (pp. 79–82). Bucharest, Romania. 10.1109/ICCOMM.2010.5509116 [PDF]
  70. A. Kabir, J. Barker, & M. Giurgiu. (2010). Integrating hidden Markov model and PRAAT: a toolbox for robust automatic speech transcription. In Proceedings of SPIE 7745, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (Vol. 774513). Wilga, Poland. 10.1117/12.872211
  71. N. Ma, J. Barker, H. Christensen, & P. Green. (2010). Distant microphone speech recognition in a noisy indoor environment: combining soft missing data and speech fragment decoding. In Proceedings of the ISCA Tutorial and Research Workshop on Statistical And Perceptual Audition. Makuhari, Japan.
  72. H. Christensen, N. Ma, S. N. Wrigley, & J. Barker. (2009). A speech fragment approach to localising multiple speakers in reverberant environments. In Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4593–4596). Taipei, Taiwan: IEEE. 10.1109/ICASSP.2009.4960653 [PDF]
  73. H. Christensen, & J. Barker. (2009). Using location cues to track speaker changes from mobile, binaural microphones. In Proceedings of the 10th Annual Conference of the International Speech Communication Association (Interspeech 2009). Brighton, UK.
  74. E. Arnaud, H. Christensen, Y-C. Lu, J. Barker, V. Khalidov, M. Hansard, … R. Horaud. (2008). The CAVA Corpus: Synchronised Stereoscopic and Binaural Datasets with Head Movements. In ICMI ’08 Proceedings of the 10th international conference on Multimodal interfaces (pp. 109–116). Crete, Greece. 10.1145/1452392.1452414 [PDF]
  75. N. Ma, J. Barker, & P. Green. (2007). Applying duration constraints by using unrolled HMMs. In Proceedings of the 8th Annual Conference of the International Speech Communication Association (Interspeech 2007). Antwerp, Belgium.
  76. H. Christensen, N. Ma, S. Wrigley, & J. Barker. (2007). Integrating pitch and localisation cues at a speech fragment level. In Proceedings of the 8th Annual Conference of the International Speech Communication Association (Interspeech 2007). Antwerp, Belgium.
  77. X. Shao, & J. Barker. (2007). Audio-visual speech fragment decoding. In Proceedings of the International Conference on Auditory-Visual Speech Processing (AVSP 2007). Hilvarenbeek, The Netherlands. [PDF]
  78. J. Barker, A. Coy, N. Ma, & M. Cooke. (2006). Recent advances in speech fragment decoding techniques. In Proceedings of the 9th International Conference on Spoken Language Processing (Interspeech 2006) (pp. 85–88). Pittsburgh, PA.
  79. K. J. Palomäki, G. J. Brown, & J. Barker. (2006). Recognition of reverberant speech using full cepstral features and spectral missing data. In Proceedings of the 2006 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Toulouse, France: IEEE. 10.1109/ICASSP.2006.1660014 [PDF]
  80. X. Shao, & J. Barker. (2006). Audio-visual speech recognition in the presence of a competing speaker. In Proceedings of the 9th International Conference on Spoken Language Processing (Interspeech 2006) (pp. 1292–1295). Pittsburgh, PA.
  81. A. Coy, & J. Barker. (2006). A Multipitch Tracker for Monaural Speech Segmentation. In Proceedings of the 9th International Conference on Spoken Language Processing (Interspeech 2006) (pp. 1678–1681). Pittsburgh, PA.
  82. G. J. Brown, S. Harding, & J. Barker. (2006). Speech separation based on the statistics of binaural auditory features. In Proceedings of the 2006 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Toulouse, France: IEEE. 10.1109/ICASSP.2006.1661434 [PDF]
  83. Barker, J. (2005). Tracking Facial Markers with an Adaptive Marker Collocation Model. In Proceedings of the 2005 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 665–668). Philadelphia, PA: IEEE. 10.1109/ICASSP.2005.1415492 [PDF]
  84. A. Coy, & J. Barker. (2005). Soft Harmonic Masks for Recognising Speech in the Presence of a Competing Speaker. In Proceedings of the 9th European Conference on Speech Communication and Technology (Interspeech 2005) (pp. 2641–2644). Lisbon, Portugal.
  85. A. Coy, & J. Barker. (2005). Recognising Speech in the Presence of a Competing Speaker using a ‘Speech Fragment Decoder.’ In Proceedings of the 2005 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 425–428). Philadelphia, PA: IEEE. 10.1109/ICASSP.2005.1415141 [PDF]
  86. S. Harding, J. Barker, & G. Brown. (2005). Mask Estimation Based on Sound Localisation for Missing Data Speech Recognition. In Proceedings of the 2005 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 537–540). Philadelphia, PA: IEEE. 10.1109/ICASSP.2005.1415169 [PDF]
  87. J. Barker, & A. Coy. (2005). Towards Solving the Cocktail Party Problem through Primitive Grouping and Model Combination. In Proceedings of Forum Acusticum. Budapest, Hungary.
  88. S. Harding, J. Barker, & G. Brown. (2005). Binaural Feature Selection for Missing Data Speech Recognition. In Proceedings of the 9th European Conference on Speech Communication and Technology (Interspeech 2005) (pp. 1269–1272). Lisbon, Portugal.
  89. Brown, G. J., Palomäki, K., & Barker, J. (2004). A Missing Data Approach for Robust Automatic Speech Recognition in the Presence of Reverberation. In Proceedings of the 18th International Congress on Acoustics (ICA) (pp. 449–452). Kyoto, Japan.
  90. K. J. Palomäki, G. J. Brown, & J. Barker. (2002). Missing data speeech recognition in reverberant conditions. In Proceedings of the 2002 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. I-65–I-68). Orlando, FL: IEEE. 10.1109/ICASSP.2002.5743655 [PDF]
  91. J. Barker, M. Cooke, & D. Ellis. (2002). Temporal integration as a consequence of multi-source decoding. In Proceedings of the ISCA Workshop on the Temporal Integration in the Perception of Speech (TIPS). Aix-en-Provence, France.
  92. J. Barker, M. Cooke, & P. Green. (2001). Robust ASR based on clean speech models: An evaluation of missing data techniques for connected digit recognition in noise. In Proceedings of the 7th European Conference on Speech Communication and Technology, 2nd INTERSPEECH Event, Eurospeech 2001 (pp. 213–216). Aalborg, Denmark. [PDF]
  93. J. Barker, M. Cooke, & D. Ellis. (2001). Combining bottom-up and top-down constraints for robust ASR: The multisource decoder. In Proceedings of Workshop on consistent and reliable acoustic cues for sound analysis (CRAC-01). Aalborg, Denmark. [PDF]
  94. P. Green, J. Barker, M. P. Cooke, & L. Josifovski. (2001). Handling Missing and Unreliable Information in Speech Recognition. In Proceedings of the 8th International Workshop on Artificial Intelligence and Statistics (AISTATS-2001). Key West, FL. [PDF]
  95. G. J. Brown, D. L. Wang, & J. Barker. (2001). A neural oscillator sound separator for missing data speech recognition. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2001) (Vol. 4, pp. 2907–2912). Washington, DC. 10.1109/IJCNN.2001.938839 [PDF]
  96. A. C. Morris, J. Barker, & H. Bourlard. (2001). From Missing Data to Maybe Useful Data: Soft Data Modelling for Noise Robust ASR. In Proceedings of the Worshop on Innovation in Speech Processing (WISP 2001). Stratford-upon-Avon, UK. [PDF]
  97. J. Barker, P. Green, & M. P. Cooke. (2001). Linking Auditory Scene Analysis and Robust ASR by Missing Data Techniques. In Proceedings of the Worshop on Innovation in Speech Processing (WISP 2001). Stratford-upon-Avon, UK. [PDF]
  98. J. Barker, M. P. Cooke, & D. P. W. Ellis. (2000). Decoding speech in the presence of other sound sources. In Proceedings of the International Conference on Spoken Language Processing. Beijing, China. [PDF]
  99. J. Barker, L. Josifovski, M. P. Cooke, & P. D. Green. (2000). Soft decisions in missing data techniques for robust automatic speech recognition. In Proceedings of the 6th International Conference on Spoken Language Processing (Interspeech 2000). Beijing, China. [PDF]
  100. Barker, J. P., & Berthommier, F. (1999). Estimation of speech acoustics from visual speech features: A comparison of linear and non-linear models. In Proceedings of the ISCA Workshop on Auditory-Visual Speech Processing (AVSP) ’99. University of California, Santa Cruz. [PDF]
  101. Barker, J. P., & Berthommier, F. (1999). Evidence of correlation between acoustic and visual features of speech. In Proc. ICPhS ’99. San Francisco. [PDF]
  102. Barker, J. P., Berthommier, F., & Schwartz, J. L. (1998). Is primitive AV coherence an aid to segment the scene? In Proceedings of the ISCA Workshop on Auditory-Visual Speech Processing (AVSP) ’98. Sydney, Australia. [PDF]
  103. Barker, J. P., Williams, G., & Renals, S. (1998). Acoustic confidence measures for segmenting broadcast news. In Proc. ICSLP ’98. Sydney, Australia. [PDF]
  104. Barker, J. P., & Cooke, M. P. (1997). Modelling the recognition of spectrally reduced speech. In Proceeding of the Eurospeech ’97 (pp. 2127–2130). Rhodes, Greece. [PDF]
  105. Barker, J. P., & Cooke, M. P. (1997). Is the sine-wave cocktail party worth attending? In Proceedings of the 2nd Workshop on Computational Auditory, Scene Analysis. Nagoya, Japan: Int. Joint Conf. Artificial Intelligence. [PDF]

Book Chapters

  1. Barker, J., Marxer, R., Vincent, E., & Watanabe, S. (2017). The CHiME challenges: Robust speech recognition in everyday environments. In S. Watanabe, M. Delcroix, F. Metze, & J. R. Hershey (Eds.), New era for robust speech recognition – Exploiting deep learning.
  2. Mandel, M. I., & Barker, J. P. (2017). Multichannel spatial clustering using model-based source separation. In S. Watanabe, M. Delcroix, F. Metze, & J. R. Hershey (Eds.), New era for robust speech recognition – Exploiting deep learning.
  3. Barker, J. P. (2013). Missing Data Techniques: Recognition with Incomplete Spectrograms. In T. Virtanen, R. Singh, & B. Raj (Eds.), Techniques for Noise Robustness in Automatic Speech Recognition (pp. 371–398). Wiley. 10.1002/9781118392683.ch14
  4. Cooke, M. P., Barker, J. P., & Lecumberri Garcia, M. L. (2013). Crowdsourcing in Speech Perception. In M. Eskanazi, G.-A. Levow, H. Meng, G. Parent, & D. Sundermann (Eds.), Crowdsourcing for Speech Processing (pp. 137–169). John Wiley and Sons.
  5. Barker, J. P. (2006). Robust automatic speech recognition. In D.-L. Wang & G. J. Brown (Eds.), Computational Auditory Scene Analysis: Principals, Algorithms and Applications (pp. 297–350). Wiley/IEEE Press.

Abstracts

  1. Leglaive, S., Fraticelli, M., ElGhazaly, H., Borne, L., Sadeghi, M., Wisdom, S., … Barker, J. P. (2024). Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge.
  2. Graetzer, S. N., Akeroyd, M. A., Barker, J., Cox, T. J., Culling, J., Naylor, G., … Viveros Munoz, R. (2020, December). Clarity : machine learning challenges to revolutionise hearing device processing. online: Proceedings of e-Forum Acusticum 2020.
  3. Graetzer, S. N., Cox, T. J., Barker, J., Akeroyd, M. A., Culling, J., & Naylor, G. (2020, January). Machine learning challenges to revolutionise hearing device processing. Toulouse, France: Proceedings of the 12th Speech in Noise Workshop (SpIN 2020).
  4. Ma, N., Brown, G., Barker, J., & Stone, M. (2017, August). Exploiting deep learning to inform spectral contrast enhancement for hearing-impaired listeners. Stockholm, Sweden: Proceedings of the 1st ISCA International Workshop on Challenges in Hearing Assistive Technology (CHAT-2017).
  5. Cooke, M., Marxer, R., Garcia Lecumberri, M. L., & Barker, J. (2017, January). Lexical frequency effects in noise-induced robust misperceptions. Oldenburg, Germany: Proceedings of the 9th Speech in Noise Workshop (SpIN 2017).
  6. H. Christensen, J. Barker, Lu, Y.-C., J. Xavier, R. Caseiro, & H. Araújo. (2009). POPeye: Real-time, binaural sound source localisation on an audio-visual robot-head. Proceedings of the Conference on Natural Computing and Intelligent Robotics and NCAF.
  7. H. Christensen, J. Barker, Lu, Y.-C., J. Xavier, R. Caseiro, & H. Araújo. (2009). POPeye: Real-time, binaural sound source localisation on an audio-visual robot-head. Proceedings of the Conference on Natural Computing and Intelligent Robotics and NCAF.
  8. H. Christensen, & J. Barker. (2009). Simultaneous Tracking of Perceiver Movements and Speaker Changes Using Head-Centered, Binaural Data. Proceedings of the Conference on Natural Computing and Intelligent Robotics and NCAF.

PhD Thesis

  1. Barker, J. P. (1998). The relationship between auditory organisation and speech perception: Studies with spectrally reduced speech (PhD thesis). Sheffield University, U.K.