LEADER 03148nam 2200553 a 450 001 9910437917803321 005 20200520144314.0 010 $a1-283-62435-4 010 $a9786613936806 010 $a1-4614-4593-0 024 7 $a10.1007/978-1-4614-4593-7 035 $a(CKB)2670000000246740 035 $a(EBL)994367 035 $a(OCoLC)811563982 035 $a(SSID)ssj0000767215 035 $a(PQKBManifestationID)11445975 035 $a(PQKBTitleCode)TC0000767215 035 $a(PQKBWorkID)10739575 035 $a(PQKB)11271498 035 $a(DE-He213)978-1-4614-4593-7 035 $a(MiAaPQ)EBC994367 035 $a(PPN)168300710 035 $a(EXLCZ)992670000000246740 100 $a20120723d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aTowards adaptive spoken dialog systems /$fAlexander Schmitt, Wolfgang Minker 210 $aNew York $cSpringer$d2013 215 $a1 online resource (257 p.) 300 $aDescription based upon print version of record. 311 $a1-4614-4592-2 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 606 $aSpeech processing systems 615 0$aSpeech processing systems. 676 $a621.39/9 700 $aSchmitt$b Alexander$01064249 701 $aMinker$b Wolfgang$0935692 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437917803321 996 $aTowards adaptive spoken dialog systems$92537161 997 $aUNINA