Overlapping speech detection using high-level information features
MA Yong1,2, BAO Changchun1
1. School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China;
2. School of Physics and Electronic Engineering, Jiangsu Normal University, Xuzhou 221009, China
Abstract：Overlapping speech is one of the main factors influencing the performance of speaker segmentation. This paper presents an overlapping speech detection method using a high-level information feature to improve the speaker segmentation results. A linguistic high-level information feature of the speech is extracted using the universal background model (UBM). Then, a hidden Markov model (HMM) is trained using the Mel frequency cepstral coefficients (MFCC) and the high-level information to detect overlapping speech. The result is then used for the speaker segmentation of the pre-processed speech. Tests on a dataset generated from the TIMIT database show that the error ratio for overlapping speech detection is significantly lower than the reference method using just the MFCC feature. The speaker segmentation is also significantly improved.
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MA Yong, BAO Changchun. Overlapping speech detection using high-level information features. Journal of Tsinghua University(Science and Technology), 2017, 57(1): 79-83.
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