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Speech synthesis from ECoG using densely connected 3D convolutional neural networks
Open accessPublished version of a preprint
Abstract
Objective . Direct synthesis of speech from neural signals could provide a fast and natural way of communication to people with neurological diseases. Invasively-measured brain activity (electrocorticography; ECoG) supplies the necessary temporal and spatial resolution to decode fast and complex processes such as speech production. A number of impressive advances in speech decoding using neural signals have been achieved in recent years, but the complex dynamics are still not fully understood. However, it is unlikely that simple linear models can capture the relation between neural activity and continuous spoken speech. Approach . Here we show that deep neural networks can be used to map ECoG from speech production areas onto an intermediate representation of speech (logMel spectrogram). The proposed method uses a densely connected convolutional neural network topology which is well-suited to work with the small amount of data available from each participant. Main results . In a study with six participants, we achieved correlations up to r = 0.69 between the reconstructed and original logMel spectrograms. We transfered our prediction back into an audible waveform by applying a Wavenet vocoder. The vocoder was conditioned on logMel features that harnessed a much larger, pre-existing data corpus to provide the most natural acoustic output. Significance . To the best of our knowledge, this is the first time that high-quality speech has been reconstructed from neural recordings during speech production using deep neural networks.
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“Previous work in speakers with intact speech has demonstrated that intelligible speech can be synthesized from neural activity during vocalized or mimed speech10,11, but this has not been shown with someone who is paralysed.”
From A high-performance neuroprosthesis for speech decoding and avatar control (2023) · cites this paper as [10]
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“Another approach, pursued by Angrick et al.111 and Anumanchipalli et al.110, involved a two-stage decoding process. In the first stage, a deep-learning model is trained to regress neural activity (recorded from SEEG111 or ECoG110) to a time series of acoustic features, such as the mel-spectrogram (Fig. 3b). In the second stage, a speech synthesizer — a vocoder — is used to convert the acoustic rep”
From The speech neuroprosthesis (2024) · cites this paper as [111]
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Cites
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Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions
Shen, J., Pang, R., Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., Chen, Z., Zhang, Y., Wang, Y., Skerrv-Ryan, R., et al. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8461368 · Cited by 2,791
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Neuroprosthesis for Decoding Speech in a Paralyzed Person with Anarthria
Moses, D. A., Metzger, S. L., Liu, J. R., Anumanchipalli, G. K., Makin, J. G., Sun, P. F., Chartier, J., Dougherty, M. E., Liu, P. M., Abrams, G. M., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2027540 · Cited by 582
A high-performance neuroprosthesis for speech decoding and avatar control
Metzger, S. L., Littlejohn, K. T., Silva, A. B., Moses, D. A., Seaton, M. P., Wang, R., Dougherty, M. E., Liu, J. R., Wu, P., Berger, M. A., et al. (2023) · Nature · doi:10.1038/s41586-023-06443-4 · Cited by 522
Decoding speech perception from non-invasive brain recordings
Défossez, A., Caucheteux, C., Rapin, J., Kabeli, O., & King, J.-R. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00714-5 · Cited by 322
He, B., Yuan, H., Meng, J., & Gao, S. (2020) · Springer eBooks · doi:10.1007/978-3-030-43395-6_4 · Cited by 150
Silent Speech Interfaces for Speech Restoration: A Review
González, J. A., Gomez-Alanis, A., Martín-Doñas, J. M., Pérez-Córdoba, J. L., & Gomez, A. M. (2020) · IEEE Access · doi:10.1109/access.2020.3026579 · Cited by 147
Metadata
- Authors
- Angrick, M., Herff, C., Mugler, E., Tate, M. C., Slutzky, M. W., Krusienski, D. J., & Schultz, T.
- Affiliations
- University of Bremen (DE)
- Northwestern University (US)
- Virginia Commonwealth University (US)
- Corresponding author
- Angrick, Miguel
- Herff, Christian
- ORCID
- Venue
- Journal of Neural Engineering
- Publisher
- IOP Publishing
- Published
- 2019-04-16
- Volume
- 16
- Issue
- 3
- Pages
- 036019
- ISSN
- 1741-2552 (ISSN-L)
- 1741-2560
- PMC
- PMC6822609
- OpenAlex
- W2952609044
- Cited by
- 225
- References
- 76
- Citations by year
- 2026: 18 · 2025: 36 · 2024: 35 · 2023: 28 · 2022: 34 · 2021: 30 · 2020: 29 · 2019: 15
- FWCI
- 12.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Neural Engineering (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- National Science Foundation (1608140)
- National Institutes of Health (F32DC015708)
- National Center for Advancing Translational Sciences (UL1TR000150)
- Bundesministerium für Bildung und Forschung (01GQ1602)
- Doris Duke Charitable Foundation (2011039)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- speech synthesis, electrocorticography, neural speech decoding, 3D convolutional neural networks, speech production, brain-computer interface, augmentative and alternative communication
- MeSH terms
- Electrocorticography, Cerebral Cortex, Communication Aids for Disabled, Communication Devices for People with Disabilities, Humans, Photic Stimulation, Speech, Neural Networks, Computer
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