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研究生:白鎮宇
研究生(外文):Chen-yu Pai
論文名稱:連續語音之情緒轉折分析與偵測
論文名稱(外文):Analysis and Detection of Emotion Change in Continuous Speech
指導教授:包蒼龍包蒼龍引用關係
指導教授(外文):Tsang-Long Pao
學位類別:碩士
校院名稱:大同大學
系所名稱:資訊工程學系(所)
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:47
中文關鍵詞:情緒辨識連續語音
外文關鍵詞:emotion recognitioncontinuous speech
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語言的溝通在人與人的交流中扮演著一個很重要的角色,人類的語音中不僅僅包含著人們所要表達的意思,還包含了這個人在當下的一種情緒表現,在這篇論文中我們保留語音辨識中常見的特徵參數做了深入探討,這些參數包含基頻、語音的抖動、線性預測參數、線性預測倒頻譜係數、梅爾頻率倒頻譜係數、指數頻率強度參數以及感知的線性預測參數,我們希望能夠在這些特徵的數值中找出一些訊息,而我們所使用的分析方法包含循序的前序選擇法以及循序的後序選擇法,另外再加上特徵權重的方法於K最近相鄰分類法(KNN),找出一組較好的特徵參數組合,在這種分類法下找出了32個具有最好的辨識效果特徵值組合,運用這些參數及分類法對我們所使用的資料庫有84%的辨識率,最後我們比較SVM的辨識率以及使用特徵權重後的KNN跟WDKNN的辨識率並且將這組32個的特徵參數組合套用在連續語音情緒辨識的系統中。
Speech communication plays an important role for human beings. Human speech is not only involving the syntax but also the feeling at the moment. In this thesis we use 11 kinds of speech features, including formant, shimmer, jitter, Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), Mel-Frequency Cepstral Coefficients (MFCC), first derivative of MFCC (D-MFCC), second derivative of MFCC (DD-MFCC), Log Frequency Power Coefficients (LFPC), Perceptual Linear Prediction (PLP) and RelAtive SpecTrAl PLP (RastaPLP) as the features for emotion classification. These features are usually used in the speech recognition. We try to find the relation between emotion and these features. The methods that we analyze the features are called sequential forward selection (SFS) and sequential backward selection (SBS). Under the KNN classifier, 32 features was chosen, and we get a recognition rate of 84% using our emotion corpus database. We also use the weighted KNN and WDKNN classification method to classify the emotion in the speech. We compare the performance of SVM with respect to weighted KNN and WDKNN. These 32 features are the most appropriate features in the emotion recognition and are used in the continuous speech emotion recognition system.
ACKNOWLEDGMENTS i
ABSTRACT ii
摘要 iii
TABLE OF CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES vii
CHAPTER 1 INTRODUCTION 1
1.1 Introduction 1
1.2 Motivation and Objective 2
1.3 Thesis Organization 4
CHAPTER 2 BACKGROUND 5
2.1 Emotion Category 5
2.2 Related Works on Emotional Speech Recognition 9
CHAPTER 3 CONTINUOUS SPEECH SEGMENTATION AND EMOTION RECOGNITION 17
3.1 Preprocessing 17
3.1.1 Short-Time Speech Processing 18
3.1.2 Normalization 18
3.2 Framing and Windowing 19
3.3 Speech Database 20
3.4 Emotion Recognition 21
3.4.1 Feature Extraction and Selection 21
3.5 Feature Weight Learning 24
3.6 Continuous Speech Segmentation 25
CHAPTER 4 EXPERIMENTAL RESULT 31
4.1 Experimental Environment 32
4.2 Sequential Forward Selection 32
4.3 Sequential Backward Selection 35
4.4 Normalization of Feature Setsc 36
4.5 Feature Weight Learning 40
4.6 Recognition of Continuous Speech 41
CHAPTER 5 CONCLUSION 42
REFERENCE 43
[1]S. Furui, Digital Speech Processing, Synthesis, and Recognition, Marcel Dekker Inc, February 10, 1989
[2]N. Sebe, I. Cohen, T. Gevers, and T.S. Huang, “Multimodal Approaches for Emotion Recognition: A Survey,” Proceedings of SPIE, Vol. 5670, pp. 56-67, Jan. 2005
[3]馮觀富, 情緒心理學, 心理出版社, 2005
[4]Encyclopedia Britannica Online, http://www.britannica.com/
[5]D. Morrison, R. Wang, L.C. De Silva, and W.L. Xu, “Real-time Spoken Affect Classification and its Application in Call-Centers,” Proceedings of the Third International Conference on Information Technology and Applications, Vol. 1, pp. 483-487, July 2005
[6]L. Vidrascu and L. Devillers, “Annotation and Detection of Blended Emotions in Real Human-Human Dialogs Recorded in a Call Center,” IEEE International Conference on Multimedia and Expo, pp. 944 – 947, July 2005
[7]C. Breazeal, “Emotive qualities in robot speech,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 3, pp.1388- 394, 2001
[8]http://www.ai.mit.edu/projects/humanoid-robotics-group/index.html
[9]B. Schuller, G. Rigoll, and M. Lang, “Speech Emotion Recognition Combining Acoustic Features and Linguistic Information in a Hybrid Support Vector Machine-Belief Network Architecture,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, pp. 577-580, May 2004
[10]Y.M. Chen, “Investigating and Finding Meaningful Use Scenarios for Emotion-Aware Technologies,” a proposal submitted to Academia Sinica, 2006
[11]A. Ortony and T.J. Turner, “What's Basic about Basic Emotions,” Psychological Review, pp. 315-331, 1990
[12]D. Canamero and J. Fredslund, “I Show You How I Like You: Human-Robot Interaction through Emotional Expression and Tactile Stimulation,” http://www.daimi.au.dk/~chili/feelix/feelix.html, May 30, 2006
[13]http://changingminds.org/explanations/emotions/basic%20emotions.htm, May 30, 2006
[14]R. Cowie, E. Douglas-Cowie, N. Tsapatsoulis, G. Votsis, S. Kollias, W. Fellenz, and J.G. Taylor, “Emotion Recognition in Human-Computer Interaction,” IEEE Signal Processing Magazine, Vol.18(1), pp.32 – 80, Jan 2001
[15]R. Tato, R. Santos, R. Kompe, and J.M. Pardo, “Emotional Space Improves Emotion Recognition,” ICSLP, pp. 2029-2032, 2002
[16]J.H. Yeh, Emotion Recognition from Mandarin Speech Signals, Master Thesis, Tatung University, 2004
[17]J. Liscombe, J. Venditti, and J. Hirschberg, “Classifying subject ratings of emotional speech using acoustic features,” Proceedings of EuroSpeech, Geneva, Switzerland ISCA Archive 8th European Conference on Speech Communication and Technology Geneva, Switzerland, pp. 725-728, September, 2003
[18]R. Cowie and E. Douglas-Cowie, “Automatic statistical analysis of the signal and prosodic signs of emotion in speech,” Proc. 4th Int. Conf. Spoken Language Processing, pp. 1989-1992, 1996
[19]F. Dellaert, T. Polzin, and A. Waibel, “Recognizing Emotion in Speech,” ICSLP Proceedings of Fourth International Conference on Spoken Language, Vol. 3, pp. 1970-1973, Oct. 1996
[20]M.W. Bhatti, Y. Wang, and L. Guan, “A neural network approach for human emotion recognition in speech,” Proceedings of the International Symposium on Circuits and Systems, Vol. 2, pp. 181-184, May 2004
[21]D. Ververidis, C. Kotropoulos, and I. Pitas, “Automatic emotional speech classification,” IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, pp. 593-596, May 2004
[22]Z.J. Chuang and C.H. Wu, “Emotion recognition using acoustic features and textual content,” IEEE International Conference on Multimedia and Expo, Vol. 1, pp. 53-56, June 2004
[23]C.M. Lee and S.S. Narayanan, “Toward Detecting Emotions in Spoken Dialogs,” IEEE Transactions on Speech and Audio Processing, VOL. 13, pp. 293-303, March 2005
[24]S. Davis and P. Mermelstein, “Comparison of Parametric Representations for Monosyllabic Word recognition in Continuously Spoken Sentences,” IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 28, pp. 357-366, Aug. 1980
[25]T.L. Nwe, S.W. Foo, and L.C. De Silva, “Detection of Stress and Emotion in Speech Using Traditional and FFT Based Log Energy Features,” Proceedings of the Joint Conference of the Fourth International Conference on Information, Communications and Signal Processing, Vol. 3, pp. 1619-1623, Dec. 2003
[26]D.N. Jiang and L.H. Cai , “Speech emotion classification with the combination of statistic features and temporal features,” IEEE International Conference on Multimedia and Expo, Vol. 3, pp. 1967-1970, June 2004
[27]J.J. Lu, Construction and Testing of a Mandarin Emotional Speech Database and Its Application, Master Thesis, Tatung University, 2004
[28]Y.H. Chang, Emotion Recognition and Evaluation of Mandarin Speech Using Weighted D-KNN Classification, Master Thesis, Tatung University, 2005
[29]O. Segawa, K. Takeda, and F. Itakura, “Continuous speech recognition without end-point detection,” IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, pp.245-248, May 2001
[30]V.K. Prasad, T. Nagarajan, and H.A Murthy, “Continuous speech recognition using automatically segmented data at syllabic units,” 2002 6th International Conference on Signal Processing, Vol. 1, pp.235-238, Aug. 2002
[31]V.A. Petrushin, “Emotion in Speech: Recognition and Application to Call Centers,” Proceedings of the Conference on Artificial Neural Networks in Engineering, pp. 7-10, Nov. 1999
[32]L. Lu, D. Liu and H.J. Zhang “Automatic Mood Detection and Tracking of Music Audio Signals,” IEEE Transactions on Audio, Speech and Language Processing, Vol. 14, pp. 5-18, Jan. 2006
[33]A. M. Kondoz and Digital Speech: Coding for Low Bit Rate Communication Systems, John Wiley & Sons, 1994
[34]R. Gutierrez-Osuna, “Pattern Analysis for Machine Olfaction: A Review,” IEEE Sensors Journal, Vol. 2, pp. 189-202, June 2002
[35]T.L. Pao, Y.T. Chen, J.J. Lu and J.H. Yeh, “The Construction and Testing of a Mandarin Emotional Speech Database,” Proceeding of ROCLING XVI, pp. 355-363, Sep. 2004
[36]王小川, 語音訊號處理, 全華科技圖書, 2004
[37]Christopher. J. C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2(2):955-974, Kluwer Academic Publishers, Boston, 1998
[38]Yi-Lin Lin and Gang Wei, “Speech emotion recognition based on HMM and SVM,” Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on, Vol. 8, pp. 4898- 4901, Aug. 2005
[39]Qiang Shi, L.LV, and H.Chen, “Optimization of K-NN by feature weight learning,” Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on, Vol. 5, pp. 2828 - 2831, Aug. 2005
[40]Chun-Yi Lee, Speech Evaluation, Master Thesis, Department of Computer Science, National Tsing Hua University, Jun. 2002
[41]Bo-Syong Juang, Automated Recognition of Emotion in Mandarin, Master Thesis, Department of Engineering Science, National Cheng Kung University, Jun. 2002
[42]Pei-Jia Li, Emotion Recognition from Continuous Mandarin Speech Signal, Master Thesis, Department of Computer Science, Tatung University, July 2006
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