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Volume 41 Issue 10
Oct.  2015
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Journal of Beijing University of Aeronautics and Astronautics  > 2015  >  41(10): 1925-1934.
JING Shaoling, MAO Xia, CHEN Lijiang, et al. Annotations and consistency detection for Chinese dual-mode emotional speech database[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(10): 1925-1934. doi: 10.13700/j.bh.1001-5965.2014.0771(in Chinese)
Citation: JING Shaoling, MAO Xia, CHEN Lijiang, et al. Annotations and consistency detection for Chinese dual-mode emotional speech database[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(10): 1925-1934. doi: 10.13700/j.bh.1001-5965.2014.0771(in Chinese)
shu

Annotations and consistency detection for Chinese dual-mode emotional speech database

doi: 10.13700/j.bh.1001-5965.2014.0771

  • School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • Received Date: 08 Dec 2014
  • Rev Recd Date: 16 Jan 2015
  • Publish Date: 20 Oct 2015
    Abstract
  • To solve problem of lacking emotional speech database with rich emotion annotation information, a Chinese dual-mode emotional speech database which contained speech and Electroglottography (EGG) information was established. Annotation and consistency detection for the established database were conducted. Firstly, we designed detailed annotation rules and methods according to characteristics of emotional speech database and selected 5 annotators labeling emotional speech database in accordance with the rules. Secondly, in order to ensure annotation quality of emotional speech database and test the integrity of annotation rules, annotators labeled parts of utterances as a test before the official annotation, the test material comprises 280 sentences (seven emotions×two actors×twenty sentences). Finally, according to the speech annotation rules, we designed corresponding consistency detection algorithm. The results show that within the time error range of 5 ms, the annotation consistency for the same utterances which labeled by 5 annotators reaches more than 60% on average. When the time error range increased to 8 ms and 10 ms, consistency can be increased by 5% and 8% on average. The experiment indicates that 5 annotators are more consistent in understanding speech. The annotation rules we designed are more complete. The quality of emotional speech database is higher.

     

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