| 结合Bi-LSTM-CNN的语音文本双模态情感识别模型 |
| |
| 引用本文： | 王兰馨,王卫亚,程鑫.结合Bi-LSTM-CNN的语音文本双模态情感识别模型[J].计算机工程与应用,2022,58(4):192-197. |
| |
| 作者姓名： | 王兰馨 王卫亚 程鑫 |
| |
| 作者单位： | 长安大学 信息工程学院，西安 710064 |
| |
| 基金项目： | 国家重点研发计划(2018YFB1600800)。 |
| |
| 摘 要： | 针对单一模态情感识别精度低的问题,提出了基于Bi-LSTM-CNN的语音文本双模态情感识别模型算法.该算法采用带有词嵌入的双向长短时记忆网络(bi-directional long short-term memory network,Bi-LSTM)和卷积神经网络(convolutional neural networ...
|
| 关 键 词： | 语音情感识别 卷积神经网络(CNN) 长短时记忆网络(LSTM) 特征融合 |
Bimodal Emotion Recognition Model for Speech-Text Based on Bi-LSTM-CNN |
| |
| WANG Lanxin,WANG Weiya,CHENG Xin.Bimodal Emotion Recognition Model for Speech-Text Based on Bi-LSTM-CNN[J].Computer Engineering and Applications,2022,58(4):192-197. |
| |
| Authors: | WANG Lanxin WANG Weiya CHENG Xin |
| |
| Affiliation: | School of Information Engineering, Chang’an University, Xi’an 710064, China |
| |
| Abstract: | To address the problem of low accuracy of single-modal emotion recognition, a speech-text bimodal emotion recognition model algorithm based on Bi-LSTM-CNN is proposed. The algorithm uses a Bi-LSTM（bi-directional long short-term memory network） with word embedding and a CNN（convolutional neural network） to form a Bi-LSTM-CNN model for text feature extraction, and the fusion results with acoustic features are used as the input of the joint CNN model for speech emotion computation. The test results based on the IEMOCAP multimodal emotion detection dataset show that the emotion recognition accuracy reaches 69.51%, which is at least 6 percentage points better than the single text modality model. |
| |
| Keywords: | speech emotion recognition convolutional neural network(CNN) long short-term memory(LSTM) feature fusion |
| 本文献已被 维普 万方数据 等数据库收录！ |
| 点击此处可从《计算机工程与应用》浏览原始摘要信息 |
|
点击此处可从《计算机工程与应用》下载全文 |
