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Establishment of Speaker Recognition Corpus for Intelligent Attendance System

Published: 22 May 2022 Publication History

Abstract

With the rapid development of information technology, student attendance has changed from paper attendance to machine attendance, such as taking photos, scanning QR codes, positioning, etc. These attendance needs to turn on the camera to take photos, which is slightly inefficient, or turn on the positioning service. However, many people think that turning on the positioning service will infringe on personal privacy. Therefore, we need to consider a more efficient Attendance method that does not infringe on personal privacy. Voice, as a signal that can quickly obtain and contain a variety of information, can be used for class students' attendance. Speaker recognition corpus is the basis of speech speaker recognition research. Diversified, large-scale and high-quality speaker recognition corpus plays an important role in improving the performance of speaker recognition system. At present, although there are many standardized corpora, there are few corpora for student attendance scenes. Therefore, this topic studies the speaker's speech feature parameters, and selects the appropriate Chinese phrases to establish the speaker's corpus.

References

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Chen B. Research on speaker recognition feature extraction algorithm and implementation of voiceprint attendance system. Kunming University of Technology, 2014.
[2]
Reynolds D A. An overview of automatic speaker recognition technology. IEEE International Conference on Acoustics, 2011.
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Cui X. Research on Speaker Recognition Based on speech mixed features. Xihua University, 2008.
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Carcagno Samuele, Plack Christopher J. Effects of age on psychophysical measures of auditory temporal processing and speech reception at low and high levels. Hearing Research, 2021, 400:
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Liu Y, Peng Y, Li B, Chen X. Speech age estimation based on reduced dimension MFCC. Electro-acoustic technology, 2018, 42(02): 32-35.
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Li Ajun and Yin Zhigang, “Standardization of speech corpus,” Data Science Journal, vol.6, no., pp.806–812, Nov. 2007.
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Chen S, Zhao H, Chen X. Construction of corpus for learning fatigue detection from speech// 2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA). IEEE, 2017.

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ICMIP '22: Proceedings of the 2022 7th International Conference on Multimedia and Image Processing
January 2022
250 pages
ISBN:9781450387408
DOI:10.1145/3517077
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 May 2022

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Author Tags

  1. Corpus
  2. Intelligent attendance
  3. Speaker recognition

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • the science and technology planning project of Nantong City
  • the Ministry of education cooperates with universities to educate people in 2021 Project

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ICMIP 2022

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