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研究生:陳世文
研究生(外文):Shih-Wen Chen
論文名稱:情緒化自發性語音分解之立方雲線數位電路設計
論文名稱(外文):A Digital Circuit Design of the Cubic Spline for Spontaneous Emotional Speech Decomposition
指導教授:周復華周復華引用關係
指導教授(外文):Fu- Hua Chou
學位類別:碩士
校院名稱:健行科技大學
系所名稱:電腦通訊與系統工程研究所
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:2014
畢業學年度:102
語文別:中文
論文頁數:20
中文關鍵詞:立方雲線情緒化自發語音處理經驗模態分解數位平行運算電路設計現場可程式邏輯閘陣列語音辨識
外文關鍵詞:Cubic splineAn emotionalized spontaneous speechEmpirical Mode DecompositionField programmable gate array
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目前的語音辨識系統在理想的環境中的辨識率已高達九成,應用於較為複雜的環境中時會因為人類的情緒化語音以及背景音而讓辨識率低落,因為這些原因目前無法很普及的應用,所以實驗室利用經驗模態分解分解語音訊號,提取必要成分使得辨識系統不會被人類的情緒化語音以及背景音影響,導致辨識率低落,但是經驗模態分解運算的速度較為緩慢,無法應用於需要及時反應之環境中,故實驗室提出利用現場可程式化閘陣列可平行處理以及處理速度快地的優點來加速經驗模態分解運算,使不具即時性之經驗模態分解技術能運用於即時線上要求之語音辨識系統中。
情緒化自發語音者辨識技術架構包括:語音訊號處理、經驗模態分解、特徵萃取、雙模辨識等四大部份。本系統之特色在經驗模態分解分解過程中,利用立方雲線內插法,配合數位平行電路加速經驗模態分解過程,並根據此數據建立與者聲學模型與語者詞彙模型,此設計對於要求高度個人化與智慧化的數位家庭生活科技,時為重要突破。


The current speech recognition systems in an ideal environment recognition rate has reached 90% because of the human emotional tone of voice and the background and let the recognition rate is low when applied to more complex environments because of these reasons is currently not very popular applications, so the laboratory use of Empirical Mode Decomposition voice signals, making the identification system to extract the necessary ingredients are not human emotional speech and background sound effects, leading to low recognition rate, but more slowly Empirical Mode Decomposition operation, it is proposed the use of Field programmable gate array processing laboratory and the advantages of fast processing speed can be accelerated parallel computing Empirical Mode Decomposition, so no experience with real-time mode decomposition technique can be applied to real-time requirements of online speech recognition system.
The technique of speech recognition in an emotionalized spontaneous speech includes four major parts: speech signal process .Empirical Mode Decomposition. Feature extraction and dual model identification systems. Characteristics of this system is to use the cubic splines parallel to the circuit speed digital Empirical Mode Decomposition. Process and according to establishment the speaker acoustic model and language model vocabulary. This design makes the voice commands identification more accurate and the stored vocabulary voice model can also own the personal characteristic of some specified speaker simultaneously.


摘  要................ .............i
Abstract............... ............ii
誌  謝 ............................iv
目  錄 .............................v
表目錄.............................vii
圖目錄............................viii
第一章 緒論.........................1
1.1 研究動機........................1
1.2 研究目標........................1
1.3 章節概要........................2
第二章 研究背景 .....................3
2.1 語音辨識技術....................3
2.2 EMD與情緒化自發語音辨識技術 .....4
2.3 內建模態函數....................5
2.4 經驗模態分解....................6
第三章 研究方法 ....................12
3.1 立方雲線內插法.................12
3.2 平行數位電路設計...............15
第四章 結論與展望..................19
4.1 結論...........................19
4.2 展望...........................19
參考文獻 ............................20
簡 歷 ............................21



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[2]徐世霖,「雲線式訊號分解之數位電路設計與模擬」,清雲科技大學,碩士論文,民國一百年。
[3]劉于碩,「應用經驗模態分解技術於情緒化自發語音之辨識」,清雲科技大學,碩士論文,民國九十六年。
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[8]F. H. Chou, J.C. Huang ,”Apply Pipelining Empirical Mode Decomposition to Accelerate An Emotionalized Speech Processing,” Proceedings of International Conference on Wavelet Analysis and Pattern Recognition, Baoding,pp.229-234,2009.
[9]F.H. Chou & Y. S. Liu, “Speaker Recognition In An Emotionalized Spontaneous Speech Using Empirical Mode Decomposition”, Proc. of The International MultiConference of Engineers and Computer Scientists, 2007.3.21~23, Hong Kong, p.387-392. (EI)
[10]N. E. Huang, et al., “The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis, ” Proceedings of the Royal Society - Mathematical, Physical and Engineering Sciences (Series A), vol.454, no. 1971, pp. 903-995, 1998.
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[12]N. E. Huang, et al., “A Confidence Limit for the Empirical Mode Decomposition and Hilbert Spectral Analysis, ”Proceedings of the Royal Society - Mathematical, Physical and Engineering Sciences (Series A),vol. 459, no. 2037, pp. 2317-2345, 2003.
[13]X. Huang, A. Acero, and H. W. Hon, Spoken Language Processing, Prentice-Hall, Inc., 2001.
[14]P. Flandri, Matlab codes of empirical mode decomposition. Available:
http://perso.ens-lyon.fr/patrick.flandrin/emd.html.


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