Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (10): 2850-2855.DOI: 10.11772/j.issn.1001-9081.2020020148

• Artificial intelligence • Previous Articles     Next Articles

Automatic emotion annotation method of Yi language data based on double-layer features

HE Jun1, ZHANG Caiqing2, ZHANG Yunfei1, ZHANG Dehai3, LI Xiaozhen1   

  1. 1. School of Information Engineering, Kunming University, Kunming Yunnan 650214, China;
    2. School of Foreign Languages, Yunnan University, Kunming Yunnan 650206, China;
    3. School of Software, Yunnan University, Kunming Yunnan 650206, China
  • Received:2020-02-17 Revised:2020-03-27 Online:2020-10-10 Published:2020-04-17
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61263043, 61864004), the Joint Special Foundation for Basic Research of Local Universities in Yunnan Province (2017FH001-058).


何俊1, 张彩庆2, 张云飞1, 张德海3, 李小珍1   

  1. 1. 昆明学院 信息工程学院, 昆明 650214;
    2. 云南大学 外国语学院, 昆明 650206;
    3. 云南大学 软件学院, 昆明 650206
  • 通讯作者: 张彩庆
  • 作者简介:何俊(1977-),男,云南云县人,副教授,博士,CCF会员,主要研究方向:数据分析;张彩庆(1977-),女,云南云县人,讲师,硕士,主要研究方向:少数民族语言;张云飞(1986-),男,云南曲靖人,讲师,硕士,主要研究方向:软件工程;张德海(1977-),男,云南临沧人,副教授,博士,CCF会员,主要研究方向:人工智能;李小珍(1983-),女,陕西商洛人,讲师,博士,主要研究方向:物联网。
  • 基金资助:

Abstract: Most of the existing automatic emotion annotation methods only extract the single recognition feature from acoustic layer or language layer. While Yi language is affected by the factors such as too many branch dialects and high complexity, so the accuracy of automatic annotation of Yi language with single-layer emotion feature is low. Based on the features such as rich emotional affixes in Yi language, a double-layer feature fusion method was proposed. In the method, the emotional features from acoustic layer and language layer were extracted respectively, the methods of generating sequence and adding units as needed were applied to complete the feature sequence alignment, and the process of automatic emotion annotation was realized through the corresponding feature fusion and automatic annotation algorithm. Taking the speech and text data of Yi language in a poverty alleviation log database as samples, three different classifiers were used for comparative experiments. The results show that the classifier has no obvious effect on the automatic annotation results, and the accuracy of automatic annotation after the fusion of double-layer features is significantly improved, the accuracy is increased from 48.1% of acoustic layer and 34.4% of language layer to 64.2% of double-layer fusion.

Key words: Yi language, automatic annotation, emotion recognition, double-layer feature fusion, poverty alleviation

摘要: 现有的情感自动标注方法大多仅从声学层或语言层提取单一识别特征,而彝语受分支方言多、复杂性高等因素的影响,对其使用单层情感特征进行自动标注的正确率较低。利用彝语情感词缀丰富等特点,提出一种双层特征融合方法,分别从声学层和语言层提取情感特征,采用生成序列和按需加入单元的方法完成特征序列对齐,最后通过相应的特征融合和自动标注算法来实现情感自动标注过程。以某扶贫日志数据库中的彝语语音和文本数据为样本,分别采用三种不同分类器进行对比实验。结果表明分类器对自动标注结果影响不明显,而双层特征融合后的自动标注正确率明显提高,正确率从声学层的48.1%和语言层的34.4%提高到双层融合的64.2%。

关键词: 彝语, 自动标注, 情感识别, 双层特征融合, 扶贫

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