| 李虹,徐小力,吴国新,丁春艳,赵学梅.基于MFCC的语音情感特征提取研究[J].电子测量与仪器学报,2017,31(3):448-453 |
| 基于MFCC的语音情感特征提取研究 |
| Research on speech emotion feature extraction based on MFCC |
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| DOI：10.13382/j.jemi.2017.03.018 |
| 中文关键词: 东巴古籍 语音情感特征 特征参数 MFCC 短时能量 |
| 英文关键词:Dongba classic books speech emotion feature characteristic parameters MFCC short time energy |
| 基金项目:国家社科基金重大项目（12&ZD234）、现代测控技术教育部重点实验室开放课题（KF20161123205）、北京市重点实验室开放课题（KF20161123208）资助项目 |
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| Author | Institution |
| Li Hong | Mechanical & Electrical Institute, Beijing Information Science & Technology University, Beijing 100192, China |
| Xu Xiaoli | Mechanical & Electrical Institute, Beijing Information Science & Technology University, Beijing 100192, China |
| Wu Guoxin | Mechanical & Electrical Institute, Beijing Information Science & Technology University, Beijing 100192, China |
| Ding Chunyan | Minzu University of China, Beijing 100081, China |
| Zhao Xuemei | Lijiang Dongba Culture Institute, Lijiang 674100, China |
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| 摘要点击次数: 3380 |
| 全文下载次数: 17119 |
| 中文摘要: |
| 为了研究”世界记忆遗产”东巴经典古籍的音频分类, 以通过语音情感特征提取的方法分类鉴别东巴音频类别，并实现对东巴经典语音的情感状态识别, 同时提高人机交互性能, 提出采用Mel频率倒谱系数(MFCC)实现语音情感特征的提取。 通过引入MFCC的一阶差分、 二阶差分描述语音特征的动态特征, 并整合短时能量特征, 最终形成MFCC和短时能量相叠加的语音信号特征参数, 达到提取反映语音情感特征的目的。 实验验证表明, 该语音信号特征提取方法能够更明显地区分出包含在语音中的情感信息, 为语音情感特征的识别研究及东巴古籍音频分类鉴别提供理论基础。 |
| 英文摘要: |
| To research the audio classification of “memory of the world heritage”Dongba classic books, the way of speech emotion feature extraction is adopted to identify the Dongba audio categories and realize the emotional state recognition of Dongba audio.And to improve the performance of human computer interaction, the way of speech emotion feature extraction based on Mel frequency cepstrum coefficient(MFCC) is adopted.The first order difference and the second order difference are introduced to describe the dynamic characteristics of speech features. The speech signal characteristic parameters based on MFCC and the short time energy are finally formed to extract the characteristics of speech emotion feature. The experiment research shows that the characteristics of the speech signal can be more clearly differentiate emotional information contained in the speech, and lay the foundation for recognizing speech emotion and the Dongba audio classification. |
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