Inventi Impact: Audio, Speech & Music Processing
publication date: 2012/4/15

NOISE-ROBUST SPEECH FEATURE PROCESSING WITH EMPIRICAL MODE DECOMPOSITION
                  Kuo-Hau Wu, Chia-Ping Chen, Bing-Feng Yeh

ABSTRACT
In this article, a novel technique based on the empirical mode decomposition methodology for processing speech features is proposed and investigated. The empirical mode decomposition generalizes the Fourier analysis. It decomposes a signal as the sum of intrinsic mode functions. In this study, we implement an iterative algorithm to find the intrinsic mode functions for any given signal. We design a novel speech feature post-processing method based on the extracted intrinsic mode functions to achieve noise-robustness for automatic speech recognition. Evaluation results on the noisy-digit Aurora 2.0 database show that our method leads to significant performance improvement. The relative improvement over the baseline features increases from 24.0 to 41.1% when the proposed post-processing method is applied on mean-variance normalized speech features. The proposed method also improves over the performance achieved by a very noise-robust frontend when the test speech data are highly mismatched.

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