Continuous speech recognition based on ICA and geometrical learning

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

We investigate the use of independent component analysis (ICA) for speech feature extraction in digits speech recognition systems. We observe that this may be true for recognition tasks based on Geometrical Learning with little training data. In contrast to image processing, phase information is not essential for digits speech recognition. We therefore propose a new scheme that shows how the phase sensitivity can be removed by using an analytical description of the ICA-adapted basis functions. Furthermore, since the basis functions are not shift invariant, we extend the method to include a frequency-based ICA stage that removes redundant time shift information. The digits speech recognition results show promising accuracy. Experiments show that the method based on ICA and Geometrical Learning outperforms HMM in a different number of training samples.

zhangdi于2010-03-29批量导入

zhangdi于2010-03-29批量导入

IEEE Systems, Man & Cybernet TCC.; Hong Kong Polytechn Univ.; Hebei Univ.; S China Univ Technol.; Chongqing Univ.; Sun Yatsen Univ.; Harbin Inst Technol.; Int Univ Germany.

Zhejiang Univ Technol, Informat Coll, Inst Intelligent Informat Syst, Hangzhou 310032, Peoples R China; Chinese Acad Sci, Inst Semicond, Beijing 100083, Peoples R China

IEEE Systems, Man & Cybernet TCC.; Hong Kong Polytechn Univ.; Hebei Univ.; S China Univ Technol.; Chongqing Univ.; Sun Yatsen Univ.; Harbin Inst Technol.; Int Univ Germany.