@techreport{TD:100245,
	att_abstract={{In previously published work, we have proposed a novel feature extraction algorithm approximating some of the human auditory characteristics and the robustness of an alternative energy estimation scheme. Herein, we examine the proposed feature performance under additive noise and suggest how to predict the noisy cepstral coefficient deviations by estimating the subband SNR values. Then, we examine the efficiency of the proposed features in the framework of a state-of-the-art LV-CSR system, namely the AT&T WATSON system. The features are examined in a mobile, voice search task, namely the Speak4It application. The proposed feature extraction scheme increases the overall performance by 6\% relative improvement, leaving the AM and LM training fixed. Additional improvements have been reported when this frontend is combined with advanced training techniques.}},
	att_authors={dd734j, eb3134, dc860v},
	att_categories={C_IIS.9},
	att_copyright={{IEEE}},
	att_copyright_notice={{}},
	att_donotupload={true},
	att_private={false},
	att_projects={WATSONASR},
	att_tags={cepstrum analysis, robustness, speech recognition, parameter estimation,  speech processing,  error analysis},
	att_techdoc={true},
	att_techdoc_key={TD:100245},
	att_url={},
	author={Dimitrios Dimitriadis and Enrico Bocchieri and Diamantino Caseiro},
	institution={{International Conference on Acoustics, Speech and Signal Processing}},
	month={May},
	title={{An Alternative Frontend for the AT&T WATSON LV-CSR System}},
	year=2011,
}