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FEATURE MAPPING FOR SPEAKER DIARIZATION IN NOISY CONDITIONS

Citation Author(s):
Weixin Zhu, Wu Guo, Guoping Hu
Submitted by:
Weixin Zhu
Last updated:
27 February 2017 - 8:37pm
Document Type:
Poster
Document Year:
2017
Event:
Presenters:
Weixin Zhu
Paper Code:
1505
 

Speaker diarization in noisy conditions is addressed in this paper. The regression-based DNN is first adopted to map the noisy acoustic features to the clean features, and then consensus clustering of the original and mapped features is used to fuse the diarization results. The experiments are conducted on the IFLY-DIAR-II database, which is a Chinese talk show database with various noise types, such as music, applause and laughter. Compared to the baseline system using PLP features, a 21.26% relative DER improvement can be achieved using the proposed algorithm.

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