Hilbert Envelope Based Features for Far-Field Speech Recognition

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).