Abstract
In this paper, we present an effective scheme combining speech enhancement with feature post-processing to improve the robustness of speech recognition systems. At front-end, minimum mean square error log-spectral amplitude (MMSE-LSA) speech enhancement is adopted to suppress noise from noisy speech. Nevertheless this enhancement is not perfect and the enhanced speech retains signal distortion and residual noise which will affect the performance of recognition systems. Thus, at back-end, the MVA feature postprocessing is used to deal with the remaining mismatch between enhanced speech and clean speech. We have evaluated recognition performance under noisy environments using NOISEX-92 database and recorded speech signals in continuous speech recognition task. Experimental results show that our approach exhibits considerable improvements in the degraded environment.
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