Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Review of Signal Subspace Speech Enhancement and Its Application to Noise Robust Speech Recognition | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 45821 15 pages doi 2007 45821 Research Article A Review of Signal Subspace Speech Enhancement and Its Application to Noise Robust Speech Recognition Kris Hermus Patrick Wambacq and Hugo Van hamme Department of Electrical Engineering - ESAT Katholieke Universiteit Leuven 3001 Leuven-Heverlee Belgium Received 24 October 2005 Revised 7 March 2006 Accepted 30 April 2006 Recommended by Kostas Berberidis The objective of this paper is threefold 1 to provide an extensive review of signal subspace speech enhancement 2 to derive an upper bound for the performance of these techniques and 3 to present a comprehensive study of the potential of subspace filtering to increase the robustness of automatic speech recognisers against stationary additive noise distortions. Subspace filtering methods are based on the orthogonal decomposition of the noisy speech observation space into a signal subspace and a noise subspace. This decomposition is possible under the assumption of a low-rank model for speech and on the availability of an estimate of the noise correlation matrix. We present an extensive overview of the available estimators and derive a theoretical estimator to experimentally assess an upper bound to the performance that can be achieved by any subspace-based method. Automatic speech recognition ASR experiments with noisy data demonstrate that subspace-based speech enhancement can significantly increase the robustness of these systems in additive coloured noise environments. Optimal performance is obtained only if no explicit rank reduction of the noisy Hankel matrix is performed. Although this strategy might increase the level of the residual noise it reduces the risk of removing essential signal information for the recogniser s back end. Finally it is also shown that subspace filtering compares favourably to the well-known spectral subtraction technique. Copyright
