Automatic speech recognition (ASR) systems, trained on speech signals from close-talking microphones, generally fail in recognizing far-field speech. In this paper, we present a Hilbert Envelope based feature extraction technique to alleviate the artifacts introduced by room reverberations. The proposed technique is based on modeling temporal envelopes of the speech signal in narrow sub-bands using Frequency Domain Linear Prediction (FDLP). ASR experiments on far-field speech using the proposed FDLP features show significant performance improvements when compared to other robust feature extraction techniques (average relative improvement of 43 % in word error rate). © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Thomas, S., Ganapathy, S., & Hermansky, H. (2008). Hilbert envelope based features for far-field speech recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5237 LNCS, pp. 119–124). Springer Verlag. https://doi.org/10.1007/978-3-540-85853-9_11
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