Direct Speech Reconstruction From Articulatory Sensor Data by Machine Learning
- Submitting institution
-
The University of Sheffield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2496
- Type
- D - Journal article
- DOI
-
10.1109/TASLP.2017.2757263
- Title of journal
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
- Article number
- -
- First page
- 2362
- Volume
- 25
- Issue
- 12
- ISSN
- 2329-9290
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
7
- Research group(s)
-
G - Speech and Hearing
- Citation count
- 15
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The research described in this paper resulted in a collaboration with Practical Control Ltd. to develop a commercial product (contact: Managing Director, http://www.practicalcontrol.com) and the award of a patent (US 2017/0263237). The research also led to invitations to participate in EU FET project submissions in 2019 and 2020 headed by Senior researcher from the Head and Neck Oncology and Surgery department (HNOS), Stichting het Nederlands Kanker Instituut.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -