Application of Voiceprint Recognition Technology Based on Channel Confrontation Training in the Field of Information Security
With the rapid development of big data, artificial intelligence, and Internet technologies, human–human contact and human–machine interaction have led to an explosion of voice data. Rapidly identifying the speaker’s identity and retrieving and managing their speech data among the massive amount of s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/15/3309 |
| _version_ | 1797586890149330944 |
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| author | Suying Gui Chuan Zhou Hao Wang Tiegang Gao |
| author_facet | Suying Gui Chuan Zhou Hao Wang Tiegang Gao |
| author_sort | Suying Gui |
| collection | DOAJ |
| description | With the rapid development of big data, artificial intelligence, and Internet technologies, human–human contact and human–machine interaction have led to an explosion of voice data. Rapidly identifying the speaker’s identity and retrieving and managing their speech data among the massive amount of speech data have become major challenges for intelligent speech applications in the field of information security. This research proposes a vocal recognition technique based on information adversarial training for speaker identity recognition in massive audio and video data, as well as speaker identification when oriented to the information security domain. The experimental results show that the method projects data from different scene channels all onto the same space and dynamically generates interactive speaker representations. It solves the channel mismatch problem and effectively improves the recognition of the speaker’s voice patterns across channels and scenes. It is able to separate overlapping voices when multiple people speak at the same time and reduce speaker separation errors. It realizes speaker voice recognition for the information security field and achieves a recall rate of 89% in a large database, which is of practical value for the intelligent application field. |
| first_indexed | 2024-03-11T00:28:34Z |
| format | Article |
| id | doaj.art-9882aa2da32548baafe26928b7a0a5b0 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T00:28:34Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-9882aa2da32548baafe26928b7a0a5b02023-11-18T22:49:16ZengMDPI AGElectronics2079-92922023-08-011215330910.3390/electronics12153309Application of Voiceprint Recognition Technology Based on Channel Confrontation Training in the Field of Information SecuritySuying Gui0Chuan Zhou1Hao Wang2Tiegang Gao3College of Software, Nankai University, Tianjin 300100, ChinaSchool of Microelectronics, Tianjin University, Tianjin 300100, ChinaEducation Foundation of Beijing Central University for Nationalities, Beijing 100000, ChinaCollege of Software, Nankai University, Tianjin 300100, ChinaWith the rapid development of big data, artificial intelligence, and Internet technologies, human–human contact and human–machine interaction have led to an explosion of voice data. Rapidly identifying the speaker’s identity and retrieving and managing their speech data among the massive amount of speech data have become major challenges for intelligent speech applications in the field of information security. This research proposes a vocal recognition technique based on information adversarial training for speaker identity recognition in massive audio and video data, as well as speaker identification when oriented to the information security domain. The experimental results show that the method projects data from different scene channels all onto the same space and dynamically generates interactive speaker representations. It solves the channel mismatch problem and effectively improves the recognition of the speaker’s voice patterns across channels and scenes. It is able to separate overlapping voices when multiple people speak at the same time and reduce speaker separation errors. It realizes speaker voice recognition for the information security field and achieves a recall rate of 89% in a large database, which is of practical value for the intelligent application field.https://www.mdpi.com/2079-9292/12/15/3309voice recognitionchannel adversarial traininginformation security domainspeaker confirmation |
| spellingShingle | Suying Gui Chuan Zhou Hao Wang Tiegang Gao Application of Voiceprint Recognition Technology Based on Channel Confrontation Training in the Field of Information Security Electronics voice recognition channel adversarial training information security domain speaker confirmation |
| title | Application of Voiceprint Recognition Technology Based on Channel Confrontation Training in the Field of Information Security |
| title_full | Application of Voiceprint Recognition Technology Based on Channel Confrontation Training in the Field of Information Security |
| title_fullStr | Application of Voiceprint Recognition Technology Based on Channel Confrontation Training in the Field of Information Security |
| title_full_unstemmed | Application of Voiceprint Recognition Technology Based on Channel Confrontation Training in the Field of Information Security |
| title_short | Application of Voiceprint Recognition Technology Based on Channel Confrontation Training in the Field of Information Security |
| title_sort | application of voiceprint recognition technology based on channel confrontation training in the field of information security |
| topic | voice recognition channel adversarial training information security domain speaker confirmation |
| url | https://www.mdpi.com/2079-9292/12/15/3309 |
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