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
Conference NameThe 9th International Symposium on Chinese Spoken Language Processing
Source PublicationThe 9th International Symposium on Chinese Spoken Language Processing
Pages341-344
Conference Date2014-9
Conference PlaceSingapore
Abstract1; 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.
KeywordSpeech Emotion Recognition
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11847
Collection模式识别国家重点实验室_语音交互
Corresponding AuthorLinlin Chao
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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