參考文獻 |
[1]S. Furui, “An Overview of Speaker Recognition Technology,” ESCA Workshop on Automatic Speaker Recognition, Identification, pp. 1–9, 1994.
[2]呂易宸,“語音門禁系統,” 中央大學碩士論文, 民國100年.
[3]J. L. Gauvain and C. H. Lee, “Maximum a Posteriori Estimation for Multivariate Gaussian Mixture Observations of Markov Chains,” IEEE Transactions on Speech and Audio Processing, vol.2, pp. 291-298, 1994.
[4]D.A. Reynolds, T.F. Quatieri, and R.B. Dunn, “Speaker Verification Using Adapted Gaussian Mixture Models,” Digital Signal Processing, vol.10, pp. 19-41, 2000.
[5]B.H. Juang and S. Katagiri, “Discriminative Learning for Minimum Error Classification [Pattern Recognition],” Signal Processing, IEEE Transactions on, vol.40, pp.3043-3054, 1992.
[6]Y. Kida, H. Yamamoto, C. Miyajima, K. Tokuda and T.Kitamura, “Minimum Classification Error Interactive Training for Speaker Identification” IEEE International Conference on Acoustics, Speech, and Signal Processin(ICASSP), vol.1, pp. 641-644, 2005.
[7]P. Kenny, G. Boulianne, P. Ouellet and P. Dumouchel, “Joint Factor Analysis Versus Eigenchannels in Speaker Recognition,” Audio, Speech, and Language Processing, IEEE Transactions on, vol.15, pp.1435-1447, 2007.
[8]李普、郭武、戴礼荣,” 联合因子分析算法中基于信号子空间的空间变换方法”, 中國科學技术大學 电子工程与信息科學系 语音及语言信息处理国家工程实验室 合肥230027)第26卷,第8期,2013年。
[9]B. Yegnanarayana and S. P. Kishore, “AANN: An Alternative to GMM for Pattern Recognition,” Neural Network, vol.15, pp. 459–469, 2002.
[10]S. Garimella, S.H. Mallidi and H. Hermansky, “Regularized Auto-Associative Neural Networks for Speaker Verification,” Signal Processing Letters, IEEE, vol. 19, pp.841-844, 2012.
[11]M. Azeem, M. Hanmandlu, and N. Ahmad, “Generalization of Adaptive Neuro-Fuzzy Inference Systems,” IEEE Trans. Neural Network, vol.11, pp. 1332–1346, 2000.
[12]E. Mamdani, “Application of Fuzzy Logic to Approximate Reasoning using Linguistic Synthesis,” IEEE Trans. Comput., vol.C-26, pp. 1182–1191, 1977.
[13]P. Martin Larsen, “Industrial Applications of Fuzzy Logic Control,” Int. J.Man-Mach. Stud., vol.12, pp. 3–10, 1980.
[14]T. Takagi and M. Sugeno, “Fuzzy Identification of Systems and its Applications to Modeling and Control,” IEEE Trans. Syst., Man Cybern., vol.SMC-15, pp. 116–132, 1985.
[15]S. Bhardwaj, S. Srivastava, M. Hanmandlu and J.R.P. Gupta, “GFM-Based Methods for Speaker Identification,” Cybernetics, IEEE Transactions on, vol.43, pp.1047-1058, 2013.
[16]S.J.D. Prince and J.H. Elder, “Probabilistic Linear Discriminant Analysis for Inferences about Identity,” Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on, vol., pp.1-8, 2007.
[17]L. Burget, O. Plchot, S. Cumani, O. Glembek, P. Matejka and N. Brummer, “Discriminatively Trained Probabilistic Linear Discriminant Analysis for Speaker Verification,” Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on, vol., pp.4832-4835, 2011.
[18]A. Kanagasundaram, D. Dean and S. Sridharan, “Improving PLDA Speaker Verification with Limited Development Data,” Acoustics, Speech and Signal Processing (ICASSP), 2014 IEEE International Conference on, vol., pp.1665-1669, 2014.
[19]C. B. de Lima, A. Alcaim and J. A. Apolinario, “On the use of PCA in GMM and AR-Vector Models for Text Independent Speaker Verification,” 14th International Conference on Digital Signal Processing, vol.2, pp. 595-598, 2002.
[20]H. J. Song and H. S. Kim, “Bilinear Model-Based Maximum Likelihood Linear Regression Speaker Adaptation Framework,” IEEE Signal Processing Letters, vol.16, pp. 1063-1066, 2009.
[21]C. H. Huang, J. T. Chien and H. M. Wangb, “A New Eigenvoice Approach to Speaker Adaptation,” International Symposium on Chinese Spoken Language Processing, pp. 109-112, 2004.
[22]M. Tonomura, T. Kosaka and S. Matsunaga, “Speaker Adaptation Based on Transfer Vector Field Smoothing using Maximum a Posteriori Probability Estimation,” International Conference on Acoustics, Speech, and Signal Processing, vol.1, pp. 688-691, 1995.
[23]M. Ben, R. Blouet and F. Bimbot, “A Monte-Carlo Method for Score Normalization in Automatic Speaker Verification using Kullback-Leibler Distances,” IEEE International Conference on Acoustics, Speech and Signal Processing, vol.1, pp. I-689-I-692, 2002.
[24]D. Yuan, L. Liang, Z. Xian-Yu and Z. Jian, “Studies on Model Distance Normalization Approach in Text-independent Speaker Verification,” Acta Automatica Sinica, vol.35, pp. 556-560, 2009.
[25]R. Auckenthaler, M. Carey and H. Lloyd-Thomas, “Score Normalization for Text-Independent Speaker Verification Systems,” Digital Signal Processing, vol.10, pp. 42-54, 2000.
[26]吳昱宏, “粒子群演算法應用於語者模型訓練與調適之研究” 中央大學碩士論文, 民國101年.
[27]蘇樺, “粒子群演算法之語者確認系統” 中央大學碩士論文, 民國103年.
[28]D. Burton, “Text-Dependent Speaker Verification Using Vector Quantization Source Coding,” IEEE Transactions on Acoustics, Speech and Signal Processing, vol.35, pp. 133-143, 1987.
[29]Y. Bennani, “Text-Independent Talker Identification System Combining Connectionist and Conventional Models,” Neural Networks for Signal Processing [1992] II., Proceedings of the 1992 IEEE-SP Workshop, vol., pp.131-138, 1992.
[30]B. Chen, J. W. Kuo and W. H. Tsai, “Lightly Supervised and Data-Driven Approaches to Mandarin Broadcast News Transcription,” IEEE International Conference on Acoustics, Speech, and Signal Processing, vol.1, pp. I-777-80, 2004.
[31]M. Bacchiani and B. Roark, “Unsupervised Language Model Adaptation,” IEEE International Conference on Acoustics, Speech, and Signal Processing, vol.1, pp. I-224 - I-227, 2003.
[32]F. Soong, A. Rosenberg, L. Rabiner and B.H. Juang, “A Vector Quantization Approach to Speaker Recognition,” Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP ′85, vol.10, pp.387-390, 1985.
[33]A. R. Richard and F. W. Homer, “Mixture Densities, Maximum Likelihood and the Em Algorithm,” Society for Industrial and Applied Mathematics, vol.26, pp. 195-239, 1984.
[34]CS229 Lecture notes Andrew Ng “The EM algorithm” http://cs229.stanford.edu/notes/cs229-notes8.pdf.
[35]Y. Wang, “Initialization in Speaker Model Training Based on Expectation Maximization,” Image and Signal Processing (CISP), 2013 6th International Congress on, vol.03, pp.1309-1313, 2013.
[36]S. Memon, M. Lech, N. Maddage, “Information Theoretic Expectation Maximization Based Gaussian Mixture Modeling for Speaker Verification,” Pattern Recognition (ICPR), 2010 20th International Conference on, vol., pp.4536-4540, 2010.
[37]A. R. Douglas, F. Q. Thomas and B. D. Robert, “Speaker Verification Using Adapted Gaussian Mixture Models,” Digital Signal Processing, vol.10, pp. 19-41, 2000.
[38]CS229 Lecture notes Andrew Ng “Mixtures of Gaussians and the EM algorithm” http://cs229.stanford.edu/notes/cs229-notes7b.pdf.
[39]Y. Linde, A. Buzo, R.M. Gray, “An Algorithm for Vector Quantizer Design,” Communications, IEEE Transactions on, vol.28, pp.84-95, 1980.
[40]T. Hao, S.M. Chu, T.S. Huang, “Generative model-based speaker clustering via mixture of von Mises-Fisher distributions,” Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on, vol., no., pp.4101-4104, 2009.
[41]A. E. Rosenberg, J. Delong, C. H. Lee, B. H. Juang and F. K. Soong, “The Use of Cohort Normalized Scores for Speaker Recognition,” Pro. ICSL 92. Banff, pp. 599-602. 1992.
[42]C.S. Liu, H.C. Wang and C.H. Lee, “Speaker Verification using Normalized Log-Likelihood Score,” IEEE Trans.on Speech and Audio Processing, pp 57-60, 1996.
[43]吳金池,“語者辨識系統之研究” 中央大學碩士論文, 民國91年.
[44]Y.H Chao, W.H. Tsai and H.M. Wang, “Discriminative Feedback Adaptation for GMM-UBM Speaker Verification,” Chinese Spoken Language Processing, 2008. ISCSLP ′08. 6th International Symposium on, vol., pp.1-4, 2008.
[45]L. Bottou,“Stochastic Gradient Descent Tricks,” Microsoft Research, Red-mond, WA leon@bottou.org http://leon.bottou.org Abstract.
[46]The NIST Year 2001 Speaker Recognition Evaluation, Available at http://www.itl.nist.gov/iad/mig/tests/sre/2001/index.html.
[47]J. Kennedy and R. Eberhart, “Particle Swarm Optimization,” IEEE International Conference on Neural Networks, vol.4, pp.1942-1948, 1995.
[48]M. Zambrano-Bigiarini, M. Clerc, R. Rojas, “Standard Particle Swarm Optimisation 2011 at CEC-2013: a Baseline for Future PSO Improvements,” Evolutionary Computation (CEC), 2013 IEEE Congress on, vol., pp.2337-2344, 2013.
[49]“Genetic Algorithms: Theory and Applications”, Lecture Notes Second Edition — WS 2001/2002 by Ulrich Bodenhofer.
[50]D. E. Goldberg, “Genetic Algorithm in Search, Optimization and Machine Learning,” AddisonWesley Publishing Company, 1989.
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