基於時頻感知域之語音增強與辨識

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：81

、訪客IP：13.58.217.242

姓名

陳恩婷(En-ting Chen) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

基於時頻感知域之語音增強與辨識
(Speech Enhancement and Recognition based on Spectral-Temporal Receptive Fields)

相關論文

★ 基於區域權重之衛星影像超解析技術	★ 延伸曝光曲線線性特性之調適性高動態範圍影像融合演算法
★ 實現於RISC架構之H.264視訊編碼複雜度控制	★ 基於卷積遞迴神經網路之構音異常評估技術
★ 具有元學習分類權重轉移網路生成遮罩於少樣本圖像分割技術	★ 具有注意力機制之隱式表示於影像重建三維人體模型
★ 使用對抗式圖形神經網路之物件偵測張榮	★ 基於弱監督式學習可變形模型之三維人臉重建
★ 以非監督式表徵分離學習之邊緣運算裝置低延遲樂曲中人聲轉換架構	★ 基於序列至序列模型之 FMCW雷達估計人體姿勢
★ 基於多層次注意力機制之單目相機語意場景補全技術	★ 基於時序卷積網路之單FMCW雷達應用於非接觸式即時生命特徵監控
★ 視訊隨選網路上的視訊訊務描述與管理	★ 基於線性預測編碼及音框基頻週期同步之高品質語音變換技術
★ 基於藉語音再取樣萃取共振峰變化之聲調調整技術	★ 即時細緻可調性視訊在無線區域網路下之傳輸效率最佳化研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

語音辨識被廣泛被運用在日常生活中，智慧型手機的辨識系統就是一個相當好的例子。然而語音辨識系統已經發展多年，早在1970年代開始便有許多學者針對此議題發展出各種不同的方法及不錯的成果，近年來針對日常生活中的噪音干擾更是有學者提出時頻感知域來應對，發展出一套辨識參數。
本論文即是採用時頻感知域參數，在本文中提出2套語音訊號的處理方法，一個是針對噪音環境下的語音增強，另一個則是在噪音環境下的語音辨識。增強部分除了使用時頻域多從解析外更搭配溫妮濾波器，舉例而言，在訊雜比為-10 dB時我們提出的增強方法可以將訊號增強到0.28 dB且在訊雜比為0 dB時產出的結果可以增進到7.11dB跟其他相關研究比較可說是較好的進步。另一個辨識部分，採用時頻分析域和傳統的梅爾濾波器做結合，在乾淨語音中辨識率可以從只有梅爾係數的68.62%，增進到83.10%，且我們所提出的參數針對特定容易混淆的字元，提供了另一種另外判別的方法，使彼此之間較不容易辨識錯誤。此外我們考量現實環境中，噪音所帶來的影響，而我們所提出的參數皆有對抗不同噪音的穩定性。

摘要(英)

Speech recognition has been widely used in daily life, such as the recognition system in smart phones. Since 1970s a lot of recognition methods have been proposed and many of them also achieved high recognition rate. However, by considering the practical situation, noisy environments might need to be taken in to account. In recent years researches on Spectral-Temporal Receptive Fields (STRF) developed for recognition.
In this paper, STRF was further studied and applied to two applications. One was for speech enhancement and, the other was for speech recognition. For speech enhancement, the proposed method utilized STRF analysis and wiener filtering to improve the speech quality. In the noisy environment with the white noise -10 and 0 dB level, our proposed method achieved 0.28 dB and 7.11 dB respectively. Compared with other studies it is a significant improvement.
As for the speech recognition, the proposed method combined the conventional parameters MFCC and STRF features including rate and scale. In clean speech the MFCC recognition rate was about 68.62% and our proposed features could obtain 83.10% recognition rate. In addition, by considering the real-world environment that included, the impact of noise, the experimental results also showed higher recognition rates at each noise level.

關鍵字(中)

★ 時頻感知域
★ 語音增強
★ 語音辨識

關鍵字(英)

★ Spectral-Temporal Receptive Fields
★ Speech Enhancement
★ Speech Recognition

論文目次

目錄
摘　要 i
Abstract ii
致謝 iii
目錄 iv
附表目錄 viii
第一章緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 論文架構 3
第二章語音增強即辨識簡介 4
2.1 語音增強與辨識技術簡介 4
2.1.1 頻譜刪減 4
2.1.2溫尼濾波器 5
2.1.3 語音品質量測(PESQ) 6
2.2 語音辨識簡介 8
2.2.1 MFCC參數介紹 9
2.2.2 隱藏式馬可夫模型(Hidden Markov Model) 11
第三章聽覺感知模型 14
3.1 聽覺感知模型 14
3.2 初期耳蝸模型 14
3.3 大腦皮質模型 16
3.4 聽覺感知模型相關文獻介紹 20
3.4.1 聽覺感知模型語音增強文獻 20
3.4.2 聽覺感知模型語音辨識文獻 22
第四章基於大腦皮質模型下語音增強演算法 24
4.1方法與流程 24
4.2實驗環境與效能分析 32
第五章基於大腦皮質模型下語音辨識演算法 37
5.1方法與流程 37
5.2時頻域參數擷取 39
5.3實驗環境與效能分析 43
第六章結論及未來展望 50
參考文獻 52

參考文獻

參考文獻
[1] T.S. Chi, P. Ru and S. Shamma, “Multiresolution spectrotemporal analysis of complex sounds,” , Journal of the Acoustical Society of America, vol. 118, no. 2, pp 887-906, 2005.
[2] Available on: http://neural.cs.washington.edu/
[3] Z.M. Dan and F.S. Monica, “A study about MFCC relevance in emotion classification for SRoL database,” Electrical and Electronics Engineering (ISEEE ,pp.1,4, 11-13 Oct. 2013.
[4] M. Lagunas and A.I. Perez-Neira,”Spectral subtraction and spectral estimation,” IEEE Workshop on Statistical Signal and Array Processing Proceedings of the Tenth, pp.383-386, 2000.
[5] 結合雜訊抑制與帶聲語音重建之語音增強系統，廖育志。
[6] PESQ Introduction http://www.pal-acoustics.com/index.php?a=services&id=143&lang=cn
[7] L. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proceedings of the IEEE , vol.77, no.2, pp.257,286, Feb 1989.
[8] 語音訊號與系統，王小川，第二版，全華圖書，民國98年
[9] C.C. Hsu, T.E Lin, J.H. Chen and T.S. Chi, “Spectro-temporal subband Wiener filter for speech enhancement,” IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4001,4004, 25-30 March 2012
[10] J. Woojay and B. H. Juang, “Speech analysis in a model of the central auditory system,” IEEE Transactions on Audio, Speech, and Language Processing, vol. 15, no. 6, pp.1802-1817, Aug. 2008.
[11] X. Yang, K. Wang, and S. A. Shamma, “Auditory representations of acoustic signals,” IEEE Trans. Inf. Theory, vol. 38, no. 2, pp. 824–839, Mar. 1992.
[12] Y. Ephraim, “Gain-adapted hidden markov models for recognition of clean and noisy speech,” IEEE Trans. Signal Process., vol. 40, no. 6,pp. 1303–1316, Jun. 1992.
[13] C. Corredor-Ardoy, L. Lamel, M. Adda-Decker and J-L Gauvain, “Multilingual phone recognition of spontaneous telephone speech,” IEEE International Conference on Acoustics, Speech and Signal Processing, vol.1, no., pp.413,416 vol.1, 12-15 May 1998
[14] Chunghwa Digits Database. [Online] Available: http://www.aclclp.org.tw/use_mat_c.php
[15] X. Q. Zhao and J. Wang, “A new noisy speech recognition method,” in Proc. IEEE International Symposium on Communications and Information Technology, Oct. 2005, pp.292-296.
[16] S. H. Choi, H. K. Kim, and H. S. Lee, “Speech recognition using quantized LSP parameters and their transformations in digital communication,” Speech Communication, vol. 30, pp. 223-233, Apr. 2000.
[17] R. Lyon and S. Shamma, “Auditory representation of timbre and pitch,” Journal of Auditory Computations, pp 221–270.
[18] W. C. Lin, H. T. Fan and J. W. Hung, “DCT-based processing of dynamic features for robust speech recognition,” in Proc. IEEE International Symposium on Chinese Spoken Language Processing, Nov. 2010, pp.12-17.
[19] V. F. S. Alencar and A. Alcaim, “LSF and LPC - derived features for large vocabulary distributed continuous speech recognition in Brazilian Portuguese,” in Proc. Asilomar Conference on Signals, Systems and Computers, Oct. 2008, pp. 1237-1241.
[20] M. A. Ferrer, I. G. Alonso, and C. M. Travieso, “Influence of initialisation and stop criteria on HMM based recognisers,” Electronics Letters, vol.36, no.13, pp.1165-1166, Jun. 2000.
[21] D. Sarkar, “Randomness in generalization ability: a source to improve it,” IEEE Transactions on Neural Networks, vol.7, no.3, pp.676-685, May 1996.

指導教授

張寶基(Pao-chi Chang)

審核日期

2014-7-15

推文