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Volume 31 Issue 2
Dec.  2010
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Lü Guo-yun, Jiang Dong-mei, Fan Yang-yu, Zhao Rong-chun, H. Sahli, W. Verhelst. DBN Model Based Multi-stream Asynchrony Triphone for Audio-Visual Speech Recognition and Phone Segmentation[J]. Journal of Electronics & Information Technology, 2009, 31(2): 297-301. doi: 10.3724/SP.J.1146.2007.01216
Citation: Lü Guo-yun, Jiang Dong-mei, Fan Yang-yu, Zhao Rong-chun, H. Sahli, W. Verhelst. DBN Model Based Multi-stream Asynchrony Triphone for Audio-Visual Speech Recognition and Phone Segmentation[J]. Journal of Electronics & Information Technology, 2009, 31(2): 297-301. doi: 10.3724/SP.J.1146.2007.01216

DBN Model Based Multi-stream Asynchrony Triphone for Audio-Visual Speech Recognition and Phone Segmentation

doi: 10.3724/SP.J.1146.2007.01216
  • Received Date: 2007-07-23
  • Rev Recd Date: 2008-12-11
  • Publish Date: 2009-02-19
  • In this paper, a novel Multi-stream Multi-states Asynchronous Dynamic Bayesian Network based context-dependent TRIphone (MM-ADBN-TRI) model is proposed for audio-visual speech recognition and phone segmentation. The model looses the asynchrony of audio and visual stream to the word level. Both in audio stream and in visual stream, word-triphone-state topology structure is used. Essentially, MM-ADBN-TRI model is a triphone model whose recognition basic units are triphones, which captures the variations in real continuous speech spectra more accurately. Recognition and segmentation experiments are done on continuous digit audio-visual speech database, and results show that: MM-ADBN-TRI model obtains the best overall performance in word accuracy and phone segmentation results with time boundaries, and more reasonable asynchrony between audio and visual speech.
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  • Potamianos G and Neti C, et al.. Recent advances in theautomatic recognition of audiovisual speech[J].Proc. IEEE.2003, 91(9):1306-1326[2]王志明, 蔡莲红, 艾海舟. 基于数据驱动方法的汉语文本-可视语音合成. 软件学报, 2005, 16(6): 1054-1063.Wang Z M, Cai L H, and Ai H Z. Text-to-visual speech inChinese based on data-driven approach. Journal of Software,2005, 16(6): 1054-1063.[3]Nefian A, Liang L, and Pi X, et al.. Dynamic Bayesiannetworks for audio-visual speech recognition[J].EURASIP,Journal on Applied Signal Processing.2002, 2002(11):1274-1288[4]Ravyse Ilse, Jiang D M, and Jiang X Y, et al.. DBN basedmodels for audio-visual speech analysis and recognition. 2006Pacific-Rim Conference on Multimedia (PCM 2006),Hangzhou, China, Nov, 2006: 19-30.[5]L Guoyun, Jiang Dongmei, and Sahli H, et al.. A novel DBNmodel for large vocabulary continuous speech recognition andphone segmentation. International Conference on ArtificialIntelligence and Pattern Recognition (AIPR-07), Orlando,Florida, USA, July 2007: 397-402.[6]Bilmes J and Bartels C. Graphical model architectures forspeech recognition. IEEE Signal Processing Magazine, 2005,22(5): 89-100.[7]L Guoyun, Jiang Dongmei, and Zhao Rongchun, et al..Multi-stream asynchrony Dynamic Bayesian Network modelfor audio-visual continuous speech recognition. 14thInternational Conference on systems, Signals and ImageProcessing (IWSSIP 2007), Maribor, Slovenia, June, 2007,1: 437-440.[8]Young S J, Odell J, and Woodland P C. Tree-based statetying for high accuracy acoustic modeling. In ProceedingsARPA Workshop on Human Language Technology,Plainsboro, New Jersey, USA, 1994: 307-312.[9]Zhou Yi, Gu Lie, and Zhang Hongjiang. Bayesian tangentshape model: Estimating shape and pose parameters viabayesian inference. The IEEE Conference on ComputerVision and Pattern Recognition, Wisconsin, USA, June, 2003,1: 109-116.[10]Jiang D M, Xie L, and Zhao R C, et al.. Acoustic visememodeling for speech driven animation: a case study. In Proc.1st IEEE Benelux Workshop on Model based Processing andcoding of Audio (MPCA-2002), Leuven, Belgium, November,2002, 1: 49-52.
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