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实用语音情感的特征分析与识别的研究

黄程韦 赵艳 金赟 于寅骅 赵力

黄程韦, 赵艳, 金赟, 于寅骅, 赵力. 实用语音情感的特征分析与识别的研究[J]. 电子与信息学报, 2011, 33(1): 112-116. doi: 10.3724/SP.J.1146.2009.00886
引用本文: 黄程韦, 赵艳, 金赟, 于寅骅, 赵力. 实用语音情感的特征分析与识别的研究[J]. 电子与信息学报, 2011, 33(1): 112-116. doi: 10.3724/SP.J.1146.2009.00886
Huang Cheng-Wei, Zhao Yan, Jin Bin, Yu Yin-Hua, Zhao Li. A Study on Feature Analysis and Recognition of Practical Speech Emotion[J]. Journal of Electronics & Information Technology, 2011, 33(1): 112-116. doi: 10.3724/SP.J.1146.2009.00886
Citation: Huang Cheng-Wei, Zhao Yan, Jin Bin, Yu Yin-Hua, Zhao Li. A Study on Feature Analysis and Recognition of Practical Speech Emotion[J]. Journal of Electronics & Information Technology, 2011, 33(1): 112-116. doi: 10.3724/SP.J.1146.2009.00886

实用语音情感的特征分析与识别的研究

doi: 10.3724/SP.J.1146.2009.00886
基金项目: 

国家自然科学基金(60472058,60975017,51075068)和江苏省自然科学基金(BK2008291)资助课题

A Study on Feature Analysis and Recognition of Practical Speech Emotion

  • 摘要: 该文针对语音情感识别在实际中的应用,研究了烦躁等实用语音情感的分析与识别。通过计算机游戏诱发的方式采集了高自然度的语音情感数据,提取了74种情感特征,分析了韵律特征、音质特征与情感维度之间的关系,对烦躁等实用语音情感的声学特征进行了评价与选择,提出了针对实际应用环境的可拒判的实用语音情感识别方法。实验结果表明,文中采用的语音情感特征,能较好识别烦躁等实用语音情感,平均识别率达到75%以上。可拒判的实用语音情感识别方法,对模糊的和未知的情感类别的分类进行了合理的决策,在语音情感的实际应用中具有重要的意义。
  • Spellman B A and Willingham D T. Current Directions in Cognitive Science. Boston: Allyn Bacon, 2007: 1-3.[2]Picard R W. Affective Computing. Cambridge: MIT Press, 1997, Chapter 6.[3]Vinciarelli A, Pantic M, Bourlard H, and Pentland A. Social signal processing: survey of an emerging domain[J].Image Vision Computing.2009, 27(12):1743-1759[4]Cowie R, Douglas-Cowie E, Tsapatsoulis N, Votsis G, Kollias S, Fellenz W, and Taylor J G. Emotion recognition in human-computer interaction[J].IEEE Signal Processing Magazine.2001, 18(1):32-80[5]Scherer K R. Vocal communication of emotion: a review of research paradigms[J].Speech Communication.2003, 40(1/2):227-256[6]Zeng Z, Pantic M, Roisman G I, and Huang T. A survey of affect recognition methods: audio, visual and spontaneous expressions[J].IEEE Transactions on Pattern Analysis and Machine Intelligence.2009, 31(1):39-58[7]Casale S, Russo A, Scebba G, and Serrano S. Speech emotion classification using machine learning algorithms. 2008 IEEE International Conference on Semantic Computing. Santa Clara, CA, USA, Aug. 4-7, 2008: 158-165.[8]Zhao Yan, Zhao Li, Zou Cai-rong, and Yu Yin-hua. Speech emotion recognition using modified quadratic discriminatioin function[J].Journal of Electronics (China.2008, 25(6):840-844[9]韩文静, 李海峰, 韩纪庆. 基于长短时特征融合的语音情感识别方法. 清华大学学报(自然科学版), 2008, 48(S1): 708-714.Han Wen-jing, Li Hai-feng, and Han Ji-qing. Speech emotion recognition with combined short and long term features. Journal of Tsinghua University (Science and Technology), 2008, 48(S1): 708-714.[10]Pao Tsang-long, Chen Yu-te, and Yeh Jun-heng. Emotion recognition and evaluation from mandarin speech signals. International Journal of Innovative Computing, Information and Control, 2008, 4(7): 1695-1709.[11]Johnstone T. Emotional speech elicited using computer games. Fourth International Conference on Spoken Language, Philadelphia, PA, USA, 1996, Vol. 3: 1985-1988.[12]Johnstone T, Van Reekum C M, hird K, and Kirsner K, et al.. Affective speech elicited with a computer game[J].Emotion.2005, 5(4):513-518[13]王治平,赵力,邹采荣. 基于基音参数规整及统计分布模型距离的语音情感识别. 声学学报, 2006, 31(1): 28-34.Wang Zhi-ping, Zhao Li, and Zou Cai-rong. Emotion speech recognition based on modified parameter and distance of statistical model of pitch. Acta Acustica, 2006, 31(1): 28-34.[14]Tato R S, Kompe R, and Pardo J M. Emotional space improves emotion recognition. ICSLP, Denver, Colorado, USA, 2002: 2029-2032.[15]Borchert M and Dusterhoft A. Emotions in speech - experiments with prosody and quality features in speech for use in categorical and dimensional emotion recognition environments. Proceeding of NLP-KE05, Wuhan, China, 2005: 147-151.Xiao Zhong-zhe, Dellandrea E, and Dou Wei-bei, et al.. Features extraction and selection for emotional speech classification. IEEE Conference on Advanced Video and Signal Based Surveillance, Como, Italy, 2005: 411-416.[16]Ho T and Basu M. Complexity measures of supervised classification problems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 24(3): 289-300.[17]王治平. 情感语音信号特征分析与识别. [博士论文], 东南大学, 2004.[18]Wang Zhi-ping. Feature analysis and emotion recognition in emotional speech.[D.Ph. dissertation], Southeast University, 2004.
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出版历程
  • 收稿日期:  2009-06-16
  • 修回日期:  2010-10-19
  • 刊出日期:  2011-01-19

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