Speaker identification using Ultra-Wideband measurement of voice
Speaker identification using Ultra-Wideband measurement of voice
Voice identification is being increasingly adopted in various domains, including security infrastructures, intelligent home systems, and personalised digital assistants. Notably, it harbours significant promise in transforming healthcare, especially in electronic health record detecting and speech impairment monitoring such as aphasia. Current strategies such as acoustic models based on deep learning, voice bio-metrics, and spectrogram analysis, have been identified with several drawbacks including vulnerability to altered voices, susceptibility to ambient noise, and the necessity for significant computational power. In response to these issues, the authors introduce a ground-breaking method of voice identification using Ultra-Wideband (UWB) technology. This method capitalises on the micro-Doppler shifts associated with movements of the laryngeal prominence. The distinctive nature of these bio-metric traits related to speech production provides superior resistance against common pitfalls of voice identification. The proposed model leverages the high-resolution characteristics of UWB to register tiny variations in laryngeal movements produced during speech, thus forming a distinct voice profile for each speaker. Through rigorous testing, the proposed system demonstrated significant progress in voice identification, achieving close to 90% accuracy in controlled experimental settings. This breakthrough indicates that UWB-enabled voice identification could have a profound effect on medical applications, providing potential improvements in diagnosing, monitoring, possibly treating speech disorders, and thereby shaping a future of enhanced and secured healthcare services.
Biometric identification, ResNet, Speaker identification, UWB radar, Voice recognition
266-276
Li, Haoxuan
da1e9521-5f38-4d23-94b5-863dc6ba02d2
Tang, Chong
9409c6d1-69d2-4598-8b43-bbb7f51f6fe2
Vishwakarma, Shelly
50ba09b3-b2f4-4e1a-881f-ad26fbb0a1a5
Ge, Yao
46b933b5-fed7-49f2-a32b-8095a686d0a6
Li, Wenda
3a3026d2-6265-4fb9-9b4e-22082087909e
20 February 2024
Li, Haoxuan
da1e9521-5f38-4d23-94b5-863dc6ba02d2
Tang, Chong
9409c6d1-69d2-4598-8b43-bbb7f51f6fe2
Vishwakarma, Shelly
50ba09b3-b2f4-4e1a-881f-ad26fbb0a1a5
Ge, Yao
46b933b5-fed7-49f2-a32b-8095a686d0a6
Li, Wenda
3a3026d2-6265-4fb9-9b4e-22082087909e
Li, Haoxuan, Tang, Chong, Vishwakarma, Shelly, Ge, Yao and Li, Wenda
(2024)
Speaker identification using Ultra-Wideband measurement of voice.
IET Radar, Sonar and Navigation, 18 (2), .
(doi:10.1049/rsn2.12525).
Abstract
Voice identification is being increasingly adopted in various domains, including security infrastructures, intelligent home systems, and personalised digital assistants. Notably, it harbours significant promise in transforming healthcare, especially in electronic health record detecting and speech impairment monitoring such as aphasia. Current strategies such as acoustic models based on deep learning, voice bio-metrics, and spectrogram analysis, have been identified with several drawbacks including vulnerability to altered voices, susceptibility to ambient noise, and the necessity for significant computational power. In response to these issues, the authors introduce a ground-breaking method of voice identification using Ultra-Wideband (UWB) technology. This method capitalises on the micro-Doppler shifts associated with movements of the laryngeal prominence. The distinctive nature of these bio-metric traits related to speech production provides superior resistance against common pitfalls of voice identification. The proposed model leverages the high-resolution characteristics of UWB to register tiny variations in laryngeal movements produced during speech, thus forming a distinct voice profile for each speaker. Through rigorous testing, the proposed system demonstrated significant progress in voice identification, achieving close to 90% accuracy in controlled experimental settings. This breakthrough indicates that UWB-enabled voice identification could have a profound effect on medical applications, providing potential improvements in diagnosing, monitoring, possibly treating speech disorders, and thereby shaping a future of enhanced and secured healthcare services.
Text
IET Radar Sonar Navi - 2023 - Li - Speaker identification using Ultra‐Wideband measurement of voice
- Version of Record
More information
Accepted/In Press date: 2 December 2023
e-pub ahead of print date: 26 December 2023
Published date: 20 February 2024
Keywords:
Biometric identification, ResNet, Speaker identification, UWB radar, Voice recognition
Identifiers
Local EPrints ID: 503221
URI: http://eprints.soton.ac.uk/id/eprint/503221
ISSN: 1751-8784
PURE UUID: ba3ed52b-5572-4fb3-963c-ab063c0e484b
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Date deposited: 24 Jul 2025 16:39
Last modified: 21 Aug 2025 05:10
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Contributors
Author:
Haoxuan Li
Author:
Chong Tang
Author:
Shelly Vishwakarma
Author:
Yao Ge
Author:
Wenda Li
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