@techreport{TD:100252,
	att_abstract={{This paper reports on the development and advances in automatic speech recognition for the AT&T Speak4It voice-search application. With Speak4It as real-life example, we show the effectiveness of acoustic model (AM) and language model (LM) estimation (adaptation and training) on relatively small amounts of field-data. We then introduce algorithmic improvements concerning the use of sentence length in LM, of non-contextual features in AM decision-trees, and of the Teager energy in the acoustic front-end. The combination of these algorithms yields substantial accuracy improvements. LM and AM estimation on samples of field-data increases the word accuracy from 66.4% to 77.1%, a relative word error reduction of 32%. The algorithmic improvements increase the accuracy to 79.7%, an additional 11.3% relative error reduction.}},
	att_authors={eb3134, dc860v, dd734j},
	att_categories={C_IIS.11},
	att_copyright={{IEEE}},
	att_copyright_notice={{}},
	att_donotupload={true},
	att_private={false},
	att_projects={WATSONASR},
	att_tags={speech recognition,  HMM,  decision-tree clustering},
	att_techdoc={true},
	att_techdoc_key={TD:100252},
	att_url={},
	author={Enrico Bocchieri and Diamantino Caseiro and Dimitrios Dimitriadis},
	institution={{International Conference On Acoustics, Speech and Signal Processing}},
	month={December},
	title={{Speech Recognition Modeling Advances For Mobile Voice Search}},
	year=2010,
}