[1] Zhang Xinran, Song Peng, Zha Cheng, Tao Huawei, et al. Auditory attention model based on Chirpletfor cross-corpus speech emotion recognition [J]. Journal of Southeast University (English Edition), 2016, 32 (4): 402-407. [doi:10.3969/j.issn.1003-7985.2016.04.002]

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Auditory attention model based on Chirpletfor cross-corpus speech emotion recognition()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

32

Issue:

2016 4

Page:

402-407

Research Field:

Information and Communication Engineering

Publishing date:

2016-12-20

Info

Title:

Auditory attention model based on Chirpletfor cross-corpus speech emotion recognition

Author(s):

Zhang Xinran¹;  Song Peng²;  Zha Cheng¹;  Tao Huawei¹;  Zhao Li¹

¹Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing 210096, China
²School of Computer and Control Engineering, Yantai University, Yantai 264005, China

Keywords:

speech emotion recognition;  selective attention mechanism;  spectrogram feature;  cross-corpus

PACS:

TN912.34

DOI:

10.3969/j.issn.1003-7985.2016.04.002

Abstract:

To solve the problem of mismatching features in an experimental database, which is a key technique in the field of cross-corpus speech emotion recognition, an auditory attention model based on Chirplet is proposed for feature extraction. First, in order to extract the spectra features, the auditory attention model is employed for variational emotion features detection. Then, the selective attention mechanism model is proposed to extract the salient gist features which show their relation to the expected performance in cross-corpus testing. Furthermore, the Chirplet time-frequency atoms are introduced to the model. By forming a complete atom database, the Chirplet can improve the spectrum feature extraction including the amount of information. Samples from multiple databases have the characteristics of multiple components. Hereby, the Chirplet expands the scale of the feature vector in the time-frequency domain. Experimental results show that, compared to the traditional feature model, the proposed feature extraction approach with the prototypical classifier has significant improvement in cross-corpus speech recognition. In addition, the proposed method has better robustness to the inconsistent sources of the training set and the testing set.

References:

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Memo

Memo:

Biographies: Zhang Xinran(1980—), male, graduate; Zhao Li(corresponding author), male, doctor, professor, zhaoli@seu.edu.cn.
Foundation items: The National Natural Science Foundation of China(No.61273266, 61231002, 61301219, 61375028), the Specialized Research Fund for the Doctoral Program of Higher Education(No.20110092130004), the Natural Science Foundation of Shandong Province(No.ZR2014FQ016).
Citation: Zhang Xinran, Song Peng, Zha Cheng, et al. Auditory attention model based on Chirplet for cross-corpus speech emotion recognition[J].Journal of Southeast University(English Edition), 2016, 32(4):402-407.DOI:10.3969/j.issn.1003-7985.2016.04.002.

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