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001			286699
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007			cr nn 008mamaa
008			150903s2013 xxk| o |||| 0|eng d
020			_a9781447149231 _99781447149231
024	7		_a10.1007/9781447149231 _2doi
035			_avtls000339959
039		9	_a201509030841 _bVLOAD _c201404300407 _dVLOAD _y201402061013 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aTK5102.9
100	1		_aThomson, Blaise. _eautor _9315484
245	1	0	_aStatistical Methods for Spoken Dialogue Management / _cby Blaise Thomson.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2013.
300			_axvii, 136 páginas 29 ilustraciones, 5 ilustraciones en color. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringer Theses, Recognizing Outstanding Ph.D. Research, _x2190-5053
500			_aSpringer eBooks
505	0		_aDialogue system theory -- Maintaining state -- Maintaining state - optimizations -- Policy design -- Evaluation -- Parameter learning.
520			_aSpeech is the most natural mode of communication and yet attempts to build systems which support robust habitable conversations between a human and a machine have so far had only limited success. A key reason is that current systems treat speech input as equivalent to a keyboard or mouse, and behaviour is controlled by predefined scripts that try to anticipate what the user will say and act accordingly. But speech recognisers make many errors and humans are not predictable; the result is systems which are difficult to design and fragile in use. Statistical methods for spoken dialogue management takes a radically different view. It treats dialogue as the problem of inferring a user's intentions based on what is said. The dialogue is modelled as a probabilistic network and the input speech acts are observations that provide evidence for performing Bayesian inference. The result is a system which is much more robust to speech recognition errors and for which a dialogue strategy can be learned automatically using reinforcement learning. The thesis describes both the architecture, the algorithms needed for fast real-time inference over very large networks, model parameter estimation and policy optimisation. This ground-breaking work will be of interest both to practitioners in spoken dialogue systems and to cognitive scientists interested in models of human behaviour.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781447149224
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4471-4923-1 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c286699 _d286699