@techreport{TD:101041,
	att_abstract={{In the current ASR systems the presence of competing speakers greatly degrades the recognition performance. This phenomenon is getting even more prominent in the case of hands-free, far-field ASR systems like the “Smart-TV” systems, where reverberation and non-stationary noise pose additional challenges. Furthermore, speakers are, most often, not standing still while speaking. To address these issues, we propose a cascaded system that includes Time Differences of Arrival estimation, multi-channel Wiener Filtering, non-negative matrix factorization (NMF), multi-condition training, and robust feature extraction, whereas each of them additively improves the overall performance. The final cascaded system presents an average of 50% and 45% relative improvement in ASR word accuracy for the CHiME 2011(non-stationary noise) and CHiME 2012 (non-stationary noise plus speaker head movement) tasks, respectively.}},
	att_authors={dd734j, eb3134},
	att_categories={},
	att_copyright={{IEEE}},
	att_copyright_notice={{This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in 2012 {{, 2013-08-26}}{{, http://www.icassp2013.com/}}
}},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={array signal processing,  automatic speech recognition,  robustness,  acoustic noise,  non-negative matrix factorization},
	att_techdoc={true},
	att_techdoc_key={TD:101041},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101041_DS1_2013-05-27T19:27:29.970Z.docx},
	author={Dimitrios Dimitriadis and Enrico Bocchieri and gANG LIU, UNIVERSITY OF TEXAS},
	institution={{Annual Conference of International Speech Communication Association (Interspeech)}},
	month={August},
	title={{ROBUST SPEECH ENHANCEMENT TECHNIQUES FOR ASR IN ADVERSE ENVIRONMENTS}},
	year=2013,
}