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A novel speech emotion recognition algorithm based on wavelet kernel sparse classifier in stacked deep auto-encoder model

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Abstract

Since the contextual information has an important impact on the speaker’s emotional state, how to use emotion-related context information to conduct feature learning is a key problem. The existing speech emotion recognition algorithms achieve the relatively high recognition rate; these algorithms are not very good application to the real-life speech emotion recognition systems. Therefore, in order to address the abovementioned issues, a novel speech emotion recognition algorithm based on improved stacked kernel sparse deep model is proposed in this paper, which is based on auto-encoder, denoising auto-encoder, and sparse auto-encoder to improve the Chinese speech emotion recognition. The first layer of the structure uses a denoising auto-encoder to learn a hidden feature with a larger dimension than the dimension of the input features, and the second layer employs a sparse auto-encoder to learn sparse features. Finally, a wavelet-kernel sparse SVM classifier is applied to classify the features. The proposed algorithm is evaluated on the testing dataset, which contains the speech emotion data of spontaneous, non-prototypical, and long-term. The experimental results show that the proposed algorithm outperforms the existing state-of-the-art algorithms in speech emotion recognition.

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Funding

This work was supported by Chongqing Big Data Engineering Laboratory for Children, Chongqing Electronics Engineering Technology Research Center for Interactive Learning, and Project of Science and Technology Research Program of Chongqing Education Commission of China (N0. KJZD-K201801601).

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  1. School of Mathematics and Information Engineering, Chongqing University of Education, Chongqing, China

    Pengcheng Wei & Yu Zhao

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  1. Pengcheng Wei
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  2. Yu Zhao
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Correspondence to Pengcheng Wei.

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Wei, P., Zhao, Y. A novel speech emotion recognition algorithm based on wavelet kernel sparse classifier in stacked deep auto-encoder model. Pers Ubiquit Comput 23, 521–529 (2019). https://doi.org/10.1007/s00779-019-01246-9

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