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007			cr nn 008mamaa
008			120918s2013 xxu| s |||| 0|eng d
020			_a9781461445937 _9978-1-4614-4593-7
024	7		_a10.1007/978-1-4614-4593-7 _2doi
050		4	_aTK5102.9
050		4	_aTA1637-1638
050		4	_aTK7882.S65
072		7	_aTTBM _2bicssc
072		7	_aUYS _2bicssc
072		7	_aTEC008000 _2bisacsh
072		7	_aCOM073000 _2bisacsh
082	0	4	_a621.382 _223
100	1		_aSchmitt, Alexander. _eauthor.
245	1	0	_aTowards Adaptive Spoken Dialog Systems _h[electronic resource] / _cby Alexander Schmitt, Wolfgang Minker.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_aXIV, 254 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aIntroduction -- Background and Related Research -- Interaction Modeling and Platform Development -- Novel Strategies for Emotion Recognition -- Novel Approaches to Pattern-based Interaction Quality Modeling -- Statistically Modeling and Predicting Task Success -- Conclusion and Future Directions.
520			_aIn Monitoring Adaptive Spoken Dialog Systems, authors Alexander Schmitt and Wolfgang Minker investigate statistical approaches that allow for recognition of negative dialog patterns in Spoken Dialog Systems (SDS). The presented stochastic methods allow a flexible, portable and  accurate use.  Beginning with the foundations of machine learning and pattern recognition, this monograph examines how frequently users show negative emotions in spoken dialog systems and develop novel approaches to speech-based emotion recognition using hybrid approach to model emotions. The authors make use of statistical methods based on acoustic, linguistic and contextual features to examine the relationship between the interaction flow and the occurrence of emotions using non-acted  recordings several thousand real users from commercial and non-commercial SDS. Additionally, the authors present novel statistical methods that spot problems within a dialog based on interaction patterns. The approaches enable future SDS to offer more natural and robust interactions. This work provides insights, lessons and  inspiration for future research and development, not only for spoken dialog systems, but for data-driven approaches to human-machine interaction in general.
650		0	_aEngineering.
650		0	_aData mining.
650		0	_aUser interfaces (Computer systems).
650		0	_aComputer graphics.
650	1	4	_aEngineering.
650	2	4	_aSignal, Image and Speech Processing.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aUser Interfaces and Human Computer Interaction.
650	2	4	_aComputer Imaging, Vision, Pattern Recognition and Graphics.
700	1		_aMinker, Wolfgang. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461445920
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4614-4593-7
912			_aZDB-2-ENG
942			_2Dewey Decimal Classification _ceBooks
999			_c44142 _d44142