Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Vocal Effort Detection Based on Spectral Information Entropy Feature and Model Fusion

  • Chao, Hao (School of Computer Science and Technology, Henan Polytechnic University) ;
  • Lu, Bao-Yun (School of Computer Science and Technology, Henan Polytechnic University) ;
  • Liu, Yong-Li (School of Computer Science and Technology, Henan Polytechnic University) ;
  • Zhi, Hui-Lai (School of Computer Science and Technology, Henan Polytechnic University)
  • Received : 2017.03.03
  • Accepted : 2017.05.29
  • Published : 2018.02.28
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Abstract

Vocal effort detection is important for both robust speech recognition and speaker recognition. In this paper, the spectral information entropy feature which contains more salient information regarding the vocal effort level is firstly proposed. Then, the model fusion method based on complementary model is presented to recognize vocal effort level. Experiments are conducted on isolated words test set, and the results show the spectral information entropy has the best performance among the three kinds of features. Meanwhile, the recognition accuracy of all vocal effort levels reaches 81.6%. Thus, potential of the proposed method is demonstrated.

Keywords

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Fig. 1. Sorted average cepstral distances among the 5 VE levels for all phonemes.

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Fig. 2. Comparison of average cepstral distances between MFCC and SIE for the five vowels.

Table 1. Six bands and their range of frequency domain

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Table 2. Two-stage VE detection results

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Table 3. The performance of combined models

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Table 4. The performance of complementary model

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