Speech emotion recognition algorithm based on spectrogram feature extraction of deep space attention feature

Jinhua WANG; Na YING; Chendu ZHU; Zhaosen LIU; Zhedong CAI

doi:10.11959/j.issn.1000-0801.2019052

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Speech emotion recognition algorithm based on spectrogram feature extraction of deep space attention feature

research and development | 更新时间：2024-06-05

- Speech emotion recognition algorithm based on spectrogram feature extraction of deep space attention feature
- Telecommunications Science Vol. 35, Issue 7, Pages: 100-108(2019)
- 作者机构：
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- DOI：10.11959/j.issn.1000-0801.2019052
  CLC： TP18
- Published Online：2019-07，
  
  Published：20 July 2019
- 稿件说明：
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Jinhua WANG, Na YING, Chendu ZHU, et al. Speech emotion recognition algorithm based on spectrogram feature extraction of deep space attention feature[J]. Telecommunications science, 2019, 35(7): 100-108.
DOI：

Jinhua WANG, Na YING, Chendu ZHU, et al. Speech emotion recognition algorithm based on spectrogram feature extraction of deep space attention feature[J]. Telecommunications science, 2019, 35(7): 100-108. DOI： 10.11959/j.issn.1000-0801.2019052.

摘要

从语音情感特征的提取和分类建模出发，以混合卷积神经网络模型为基础，改进特征提取中的 Itti模型，包括：增加通过局部二值模式提取的纹理特征；结合听觉敏感度权重提取情感强相关特征。然后提出通过特征约束条件提取标定权重特征的约束挤压和激励网络结构；最后形成以 VGGnet 和长短时记忆网络混合网络为基础的微调模型，进一步提升了情感表征能力。通过在自然情感数据库和柏林德语数据库上进行验证，该模型在情感识别率上有明显的上升，相较于基准模型提升了 8. 43%，同时对比了本模型在自然数据库（FAU-AEC）和柏林数据库（EMO-DB）上的识别效果，实验结果证明模型具有良好的泛化性。

Abstract

Starts from the extraction and classification modeling of speech emotion features

based on the hybrid convolutional neural network model

the Itti model in feature extraction was improved

including increasing the extraction by local binary mode. The strong correlation features were extracted combining with the sensitivity of the auditory sensitivity. Then

the constrained extrusion and excitation network structure of the calibration weights were extracted by feature constraints. Finally

a fine-tuning model based on VGGnet and long-short-time memory network hybrid network was formed

further enhancing the ability to express emotions. By validating on the natural sentiment database and the German-German database

the model had a significant increase in the rate of sentiment recognition

which is 8. 43% higher than the benchmark model. At the same time

the recognition effect of the model on the natural database (FAU-AEC) and the Berlin database (EMO-DB) were compared. The experimental results show that the model has a good generalization.

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references

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