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 Transactions on Biomedical Engineering
10.1109/TBME.2022.3167113.
(In press).
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Abstract
Abstract Goal: Advances in computational models of biological systems and artificial neural networks enable rapid virtual prototyping of neuroprosthetics, 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 neuroprosthetic. 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 neuroprosthetics.
| Type: | Article |
|---|---|
| Title: | From Microphone to Phoneme: An End-to-End Computational Neural Model for Predicting Speech Perception with Cochlear Implants |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TBME.2022.3167113 |
| Publisher version: | https://doi.org/10.1109/TBME.2022.3167113 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Biological system modeling, Computational modeling, Predictive models, Speech recognition, Finite element analysis, Ear, Speech processing |
| UCL classification: | UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Speech, Hearing and Phonetic Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10156303 |
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