From Microphone to Phoneme: An End-to-End Computational Neural Model for Predicting Speech Perception with Cochlear Implants
Authors
Brochier, Tim
Roberts, iwan
Schlittenlacher, josef
Jiang, chen
Publication Date
2022-11Journal Title
IEEE Transactions on Biomedical Engineering
ISSN
0018-9294
Publisher
Institute of Electrical and Electronics Engineers
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Bance, M., Brochier, T., Vickers, D., Goehring, T., Roberts, i., Schlittenlacher, j., & Jiang, c. (2022). From Microphone to Phoneme: An End-to-End Computational Neural Model for Predicting Speech Perception with Cochlear Implants. IEEE Transactions on Biomedical Engineering https://doi.org/10.1109/TBME.2022.3167113
Abstract
Abstract— Goal: Advances in computational models of biological systems and artificial neural networks enable rapid virtual prototyping of neuroprostheses, accelerating innovation in the field. Here, we present an end-to-end computational model for predicting speech perception with cochlear implants (CI), the most widely-used neuroprosthesis. Methods: The model integrates CI signal processing, a finite element model of the electrically-stimulated cochlea, and an auditory nerve model to predict neural responses to speech stimuli. An automatic speech recognition neural network is then used to extract phoneme-level speech perception from these neural response patterns. Results: Compared to human CI listener data, the model predicts similar patterns of speech perception and misperception, captures between-phoneme differences in perceptibility, and replicates effects of stimulation parameters and noise on speech recognition. Information transmission analysis at different stages along the CI processing chain indicates that the bottleneck of information flow occurs at the electrode-neural interface, corroborating studies in CI listeners. Conclusion: An end-to-end model of CI speech perception replicated phoneme-level CI speech perception patterns, and was used to quantify information degradation through the CI processing chain. Significance: This type of model shows great promise for developing and optimizing new and existing neuroprostheses.
Index Terms— neural prostheses, cochlear implants, computational models, automatic speech recognition, signal processing, information transmission, neural networks
Sponsorship
HB Allen Trust charity
Funder references
H.B. Allen Charitable Trust (Unknown)
William Demant Foundation (Case no. 20-0390)
National Institute for Health Research (NIHR) (via Guy's and St Thomas' NHS Foundation Trust) (201608)
Wellcome Trust (204845/Z/16/Z)
Medical Research Council (MR/S002537/1)
Identifiers
External DOI: https://doi.org/10.1109/TBME.2022.3167113
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336003
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