CASIA OpenIR  > 模式识别国家重点实验室  > 语音交互
Improving generation performance of speech emotion recognition by denoising autoencoders
Linlin Chao; Jianhua Tao; Minghao Yang; Ya Li
2014
会议名称The 9th International Symposium on Chinese Spoken Language Processing
会议录名称The 9th International Symposium on Chinese Spoken Language Processing
页码341-344
会议日期2014-9
会议地点Singapore
摘要1; A speech emotion recognition algorithm should generalize well when the target person’s speech samples and prior knowledge about their emotional content are not included in the training data. In order to achieve this objective, we utilize denoising autoencoders based approach to solve this task. In this study, a relatively small dataset, which contains close to 1500 persons’ emotion sentences, is introduced. By unsupervised pre-training with this dataset, denoising autoencoders learn features which contain more emotion-specific information than speaker-specific information in data successfully. Experiment results in CASIA dataset show that this denoising autoencoders based approach can improve the generation performance of speech emotion recognition significantly.
关键词Speech Emotion Recognition
收录类别EI
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11847
专题模式识别国家重点实验室_语音交互
通讯作者Linlin Chao
作者单位Institute of Automation, Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Linlin Chao,Jianhua Tao,Minghao Yang,et al. Improving generation performance of speech emotion recognition by denoising autoencoders[C],2014:341-344.
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