원문정보
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
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