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Research on depression detection algorithm combine acoustic rhythm with sparse face recognition

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Abstract

Due to the existence of false positive rate of the traditional depression diagnosis method, this paper proposes a multi-modal fusion algorithm based on speech signal and facial image sequence for depression diagnosis. Introduced spectrum subtraction to enhance depressed speech signal, and use cepstrum method to extract pitch frequency features with large variation rate and formant features with significant difference, the short time energy and Mel-frequency cepstral coefficients characteristic parameters for different emotion speeches are analyzed in both time domain and frequency domain, and establish a model for training and identification. Meanwhile, this paper implements the orthogonal match pursuit algorithm to obtain a sparse linear combination of face test samples, and cascade with voice and facial emotions based proportion. The experimental results show that the recognition rate based on the depression detection algorithm of fusion speech and facial emotions has reached 81.14%. Compared to the existing doctor’s accuracy rate of 47.3%, the accuracy can bring extra 71.54% improvement by combining with the proposed method of this paper. Additionally, it can easily apply to the hardware and software on the existing hospital instruments with low cost. Therefore, it is an accurate and effective method for diagnosing depression.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61379010).

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Authors and Affiliations

  1. School of Information Science and Technology, Northwest University, Xi’an, 710127, China

    Jian Zhao, Weiwen Su, Chao Zhang & Tingting Lu

  2. School of Mathematics, Northwest University, Xi’an, 710127, China

    Jian Jia

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  1. Jian Zhao
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  2. Weiwen Su
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  3. Jian Jia
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  5. Tingting Lu
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Correspondence to Jian Jia.

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Zhao, J., Su, W., Jia, J. et al. Research on depression detection algorithm combine acoustic rhythm with sparse face recognition. Cluster Comput 22 (Suppl 4), 7873–7884 (2019). https://doi.org/10.1007/s10586-017-1469-0

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  • DOI: https://doi.org/10.1007/s10586-017-1469-0

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