Earticle

초록

영어

This paper proposes to estimate key speaker in meeting speech based on multiple features optimization. First, each feature is defined and their differences between key speaker and other speakers are analyzed. Then, a decision function of multiple feature weighting is generated for estimating key speaker in meeting speech, and the genetic algorithm is used to optimize these coefficients of feature weighting. The methods are evaluated on three different meeting speech datasets. Experimental results show that the proposed optimization method obtains average accuracy of 93.3% for estimating key speaker, and gains average accuracy improvement by 9.7% and 4.1% compared with the previous method and the feature weighting method without optimization, respectively.

목차

Abstract
 1. Introduction
 2. Feature Difference of Speakers
  2.1. Average Speaking Rate
  2.2. The Total Length of Speaking Time
  2.3. The Maximum Length of Single Speaking Time
  2.4. The Total Times of Speaking
 3. The Methods for Estimating the Key Speaker
  3.1. Key Speaker Estimation Method
  3.2. Optimized Method of Feature Weighting Coefficients
 4. Experimental Result and Analysis
  4.1. Experiment Setting
  4.2. Experimental Result
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Wei Li Yanxiong Li [ School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, Guangdong province, China ] Corresponding author
• Qianhua He [ School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, Guangdong province, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605