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KMID : 0917520030100030263
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Journal of Speech Sciences
2003 Volume.10 No. 3 p.263 ~ p.277
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Performance Improvement of SPLICE-based Noise Compensation for Robust Speech Recognition
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Abstract
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One of major problems in speech recognition is performance degradation due to the mismatch between the training and test environments. Recently, Stereo-based Piecewise Linear Compensation for Environments (SPLICE), which is frame-based bias removal algorithm for campestral enhancement using stereo training data and noisy speech model as a mixture of Gaussians, was proposed and showed good performance in noisy environments. In this paper, we propose several methods to improve the conventional SPLICE. First we apply Campestral Mean Subtraction (CMS) as a preprocessor to SPLICE, instead of applying it as a postprocessor. Secondly, to compensate residual distortion after SPLICE processing, two-stage SPLICE is proposed. Thirdly we employ phonetic information for training SPLICE model. According to experiments on the Aurora 2 database, proposed method outperformed the conventional SPLICE and we achieved a 50% decrease in word error rate over the Aurora baseline system.
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