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Speech corpora subset selection based on time-continuous utterances features

Author

Listed:
  • Luobing Dong

    (Xidian University)

  • Qiumin Guo

    (Beijing University of Chemical Technology)

  • Weili Wu

    (University of Texas at Dallas)

Abstract

An extremely large corpus with rich acoustic properties is very useful for training new speech recognition and semantic analysis models. However, it also brings some troubles, because the complexity of the acoustic model training usually depends on the size of the corpora. In this paper, we propose a corpora subset selection method considering data contributions from time-continuous utterances and multi-label constraints that are not limited to single-scale metrics. Our goal is to extract a sufficiently rich subset from large corpora under certain meaningful constraints. In addition, taking into account the uniform coverage of the target subset and its internal property, we design a constrained subset selection algorithm. Specifically, a fast subset selection algorithm is designed by introducing n-grams models. Experiments are implemented based on very large real speech corpora database and validate the effectiveness of our method.

Suggested Citation

  • Luobing Dong & Qiumin Guo & Weili Wu, 2019. "Speech corpora subset selection based on time-continuous utterances features," Journal of Combinatorial Optimization, Springer, vol. 37(4), pages 1237-1248, May.
    • Handle: RePEc:spr:jcomop:v:37:y:2019:i:4:d:10.1007_s10878-018-0350-2
    DOI: 10.1007/s10878-018-0350-2
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    References listed on IDEAS

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    1. Lothar Walter & Alfred Radauer & Martin G. Moehrle, 2017. "The beauty of brimstone butterfly: novelty of patents identified by near environment analysis based on text mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 103-115, April.
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    Cited by:

    1. Chu Rouxia & Chen Xiaodong & Tao Shifang & Yang Donghai, 2020. "Research on inverse simulation of physical training process based on wireless sensor network," International Journal of Distributed Sensor Networks, , vol. 16(4), pages 15501477209, April.

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