In this paper we propose a new speech parameterization framework that efficiently combines frequency and magnitude information from the short-term power spectrum of speech. This is achieved through computation of subband spectral centroid histograms (SSCH). Relationship between the proposed method and auditory based speech parameterization methods is discussed. An experimental study on an automatic speech recognition task has shown that the proposed method outperforms the conventional speech front-ends in presence of different types of additive noise, while it performs comparably in the noise-free conditions. In the case of car noise, our method also outperforms the computationally expensive auditory based methods, while having simplicity and low computational cost similar to the conventional front-ends.
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