tailieunhanh - Báo cáo hóa học: " Research Article Robust In-Car Speech Recognition Based on Nonlinear Multiple Regressions"

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 Robust In-Car Speech Recognition Based on Nonlinear Multiple Regressions | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 16921 10 pages doi 2007 16921 Research Article Robust In-Car Speech Recognition Based on Nonlinear Multiple Regressions Weifeng Li 1 Kazuya Takeda 1 and Fumitada Itakura2 1 Graduate School of Information Science Nagoya University Nagoya 464-8603 Japan 2 Department of Information Engineering Faculty of Science and Technology Meijo University Nagoya 468-8502 Japan Received 31 January 2006 Revised 10 August 2006 Accepted 29 October 2006 Recommended by S. Parthasarathy We address issues for improving handsfree speech recognition performance in different car environments using a single distant microphone. In this paper we propose a nonlinear multiple-regression-based enhancement method for in-car speech recognition. In order to develop a data-driven in-car recognition system we develop an effective algorithm for adapting the regression parameters to different driving conditions. We also devise the model compensation scheme by synthesizing the training data using the optimal regression parameters and by selecting the optimal HMM for the test speech. Based on isolated word recognition experiments conducted in 15 real car environments the proposed adaptive regression approach shows an advantage in average relative word error rate WER reductions of and compared to original noisy speech and ETSI advanced front end respectively. Copyright 2007 Weifeng Li et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION The mismatch between training and testing conditions is one of the most challenging and important problems in automatic speech recognition ASR . This mismatch may be caused by a number of factors such as background noise speaker variation a change in speaking styles channel .

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