000			03289nam a22005295i 4500
001			978-0-387-85830-2
003			DE-He213
005			20251006084426.0
007			cr nn 008mamaa
008			100301s2009 xxu| s |||| 0|eng d
020			_a9780387858302
020			_a99780387858302
024	7		_a10.1007/978-0-387-85830-2 _2doi
100	1		_aMinker, Wolfgang. _eauthor.
245	1	0	_aIncorporating Knowledge Sources into Statistical Speech Recognition _h[electronic resource] / _cby Wolfgang Minker, Satoshi Nakamura, Konstantin Markov, Sakriani Sakti.
264		1	_aBoston, MA : _bSpringer US, _c2009.
300			_bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Electrical Engineering, _x1876-1100 ; _v42
505	0		_aand Book Overview -- Statistical Speech Recognition -- Graphical Framework to Incorporate Knowledge Sources -- Speech Recognition Using GFIKS -- Conclusions and Future Directions.
520			_aIncorporating Knowledge Sources into Statistical Speech Recognition offers solutions for enhancing the robustness of a statistical automatic speech recognition (ASR) system by incorporating various additional knowledge sources while keeping the training and recognition effort feasible. The authors provide an efficient general framework for incorporating knowledge sources into state-of-the-art statistical ASR systems. This framework, which is called GFIKS (graphical framework to incorporate additional knowledge sources), was designed by utilizing the concept of the Bayesian network (BN) framework. This framework allows probabilistic relationships among different information sources to be learned, various kinds of knowledge sources to be incorporated, and a probabilistic function of the model to be formulated. Incorporating Knowledge Sources into Statistical Speech Recognition demonstrates how the statistical speech recognition system may incorporate additional information sources by utilizing GFIKS at different levels of ASR. The incorporation of various knowledge sources, including background noises, accent, gender and wide phonetic knowledge information, in modeling is discussed theoretically and analyzed experimentally.
650		0	_aENGINEERING.
650		0	_aCOMPUTER COMMUNICATION NETWORKS.
650		0	_aACOUSTICS.
650		0	_aCOMPUTER ENGINEERING.
650		0	_aTELECOMMUNICATION.
650	1	4	_aENGINEERING.
650	2	4	_aELECTRICAL ENGINEERING.
650	2	4	_aCOMPUTER COMMUNICATION NETWORKS.
650	2	4	_aCOMMUNICATIONS ENGINEERING, NETWORKS.
650	2	4	_aACOUSTICS.
650	2	4	_aSIGNAL, IMAGE AND SPEECH PROCESSING.
700	1		_aNakamura, Satoshi. _eauthor.
700	1		_aMarkov, Konstantin. _eauthor.
700	1		_aSakti, Sakriani. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387858296
830		0	_aLecture Notes in Electrical Engineering, _x1876-1100 ; _v42
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-85830-2 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-ENG
942			_2ddc _cER
999			_c59294 _d59294