Skip to main content

Conflict Cues in Call Center Interactions

  • Chapter
  • First Online:
Conflict and Multimodal Communication

Abstract

The detection of conflict cues in call center interactions may be related to the quality assessment of the services provided, since these cues reveal both speakers’ emotional states and positioning as expressed through complaining and identifying problematic issues on the one hand and managing requests or resolving problems on the other hand. This paper describes a set of emotional and conversational cues associated to conflict as well as a machine learning approach to classify emotional speech units occurring in a call center dataset by employing emotion labels as well as automatically extracted acoustic and additional context-related features.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://tla.mpi.nl/tools/tla-tools/elan/

References

  • Allwood J (2007) Cooperation, competition, conflict and communication. Gothenbg Pap Theor Linguist 94:1–14

    Google Scholar 

  • Allwood J, Cerrato L, Jokinen K, Navarretta C, Paggio P (2007) The MUMIN coding scheme for the annotation of feedback, turn management and sequencing phenomena. Multimodal corpora for modeling human multimodal behaviour. J Lang Resour Eval 41(3–4):273–287

    Article  Google Scholar 

  • Burkhardt F, Polzehl T, Stegmann J, Metze F, Huber R (2009) Detecting real life anger. In: ICASSP 2009, Taipei, Taiwan, 19–24 Apr

    Google Scholar 

  • Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3). http://www.csie.ntu.edu.tw/∼cjlin/libsvm

  • Erden M, Arslan LM (2011) Automatic detection of anger in human-human call center dialogs. In: Interspeech 2011, Florence, Italy, 28–31 Aug

    Google Scholar 

  • Esposito A, Riviello MT (2011) The cross-modal and cross-cultural processing of affective information. In: Apolloni B et al (eds) Proceedings of the 2011 Conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets. IOS Press Amsterdam, The Netherlands, pp 301–310

    Google Scholar 

  • Eyben F, Wollmer M, Schuller B (2010) openSMILE—the Munich versatile and fast open-source audio feature extractor. In: ACM multimedia, Florence, Italy, pp 1459–1462

    Google Scholar 

  • Grezes F, Richards J, Rosenber A (2013) Let me finish: automatic conflict detection using speaker overlap. In: Interspeech 2013, ISCA, Lyon, France

    Google Scholar 

  • Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor 11:10–18

    Article  Google Scholar 

  • Jahromi AT, Sepehri MM, Teimourpour B, Choobdar S (2010) Modeling customer churn in a non-contractual setting: the case of telecommunications service providers. J Strateg Mark 18(7):587–598

    Article  Google Scholar 

  • Kim S, Filippone M, Valente F, Vinciarelli A (2012) Predicting the conflict level in television political debates: an approach based on crowdsourcing, nonverbal communication and Gaussian processes. In: ACM international conference on multimedia, Nara, Japan, pp 793–796

    Google Scholar 

  • Lee CM, Narayanan S (2005) Toward detecting emotions in spoken dialogs. IEEE Trans Speech Audio Process 13(2):293–303

    Article  Google Scholar 

  • Morrison D, Wang R, De Silva LC (2007) Ensemble methods for spoken emotion recognition in call-centres. Speech Comm 49(2):98–112

    Article  Google Scholar 

  • Mower E, Mataric MJ, Narayanan S (2011) A framework for automatic human emotion classification using emotion profiles. IEEE Trans Audio Speech Lang Process 19(5):1057–1070

    Article  Google Scholar 

  • Narayanan S, Gregoriou P (2013) Behavioral signal processing: deriving human behavioral informatics from speech and language. Proc IEEE 101(5):1203–1233

    Article  Google Scholar 

  • Neiberg D, Elenius K (2008) Automatic recognition of anger in spontaneous speech. In: Interspeech 2008, Brisbane, Australia, 22–26 Sept, pp 2755–2758

    Google Scholar 

  • Pesarin A, Cristani M, Murino V, Vinciarelli A (2012) Conversation analysis at work: detection of conflict in competitive discussions through semi-automatic turn-organization analysis. Cogn Process 13(Suppl 2):533–540

    Article  Google Scholar 

  • Polzehl T, Schmitt A, Metze F, Wagner M (2011) Anger recognition in speech using acoustic and linguistic cues. Speech Comm 53(9–10):1198–1209, Special Issue on Sensing Emotion and Affect—Facing Realism in Speech Processing

    Article  Google Scholar 

  • Riviello MT, Chetouani M, Cohen D, Esposito A (2011) Inferring emotional information from vocal and visual cues: a cross-cultural comparison. In: IEEE 2nd international conference on cognitive computation, Budapest, pp 1–4

    Google Scholar 

  • Sacks H, Schegloff E, Jefferson G (1974) A simplest systematics for the organization of turn-taking in conversation. Language 50:696–735

    Article  Google Scholar 

  • Schegloff E (2000) Overlapping talk and the organisation of turn-taking for conversation. Lang Soc 29(1):1–63

    Article  Google Scholar 

  • Schröder M (ed) (2013) Emotion markup language (EmotionML) 1.0, W3C Proposed Recommendation 16 Apr 2013. http://www.w3.org/TR/emotionml/

  • Schröder M, Pelachaud C (ed) (2012) W3C vocabularies for EmotionML, W3C Working Group Note 10 May 2012. http://www.w3.org/TR/emotion-voc/

  • Schuller B et al (2007) The relevance of feature type for the automatic classification of emotional user states: low level descriptors and functionals. In: Interspeech 2007, Antwerp, pp 2253–2256

    Google Scholar 

  • Schuller B et al (2010) The INTERSPEECH 2010 paralinguistic challenge—age, gender, and affect. In: Proceedings of 11th international conference on spoken language processing, interspeech 2010—ICSLP, Makuhari, Japan, 26–30 Sept, pp 2794–2797

    Google Scholar 

  • Schuller B, Batliner A, Steidl S, Seppi D (2011) Recognising realistic emotions and affect in speech: state of the art and lessons learnt from the first challenge. Speech Comm 53(9/10):1062–1087, Special Issue on Sensing Emotion and Affect—Facing Realism in Speech Processing

    Article  Google Scholar 

  • Schuller B, Steidl S, Batliner A, Vinciarelli A, Scherer K, Ringeval F, Chetouani M, Weninger F, Eyben F, Marchi E, Salamin E, Polychroniou A, Valente F, Kim S (2013) The interspeech 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism. In: Interspeech 2013, ISCA, Lyon, France

    Google Scholar 

  • Vidrascu L, Devillers L (2007) Five emotion classes detection in real-world call center data: the use of various types of paralinguistic features. In: International workshop on Paralinguistic Speech—between models and data, ParaLing, pp 11–16

    Google Scholar 

Download references

Acknowledgments

The research leading to these results has been partially funded by POLYTROPON project (KRIPIS-GSRT, MIS: 448306). Also, the participation to Dagstuhl Seminar 13451 “Computational Audio Analysis” held from Nov 3 to 8, 2013, in Wadern, Germany, inspired Anna Esposito to contribute to this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maria Koutsombogera .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Koutsombogera, M. et al. (2015). Conflict Cues in Call Center Interactions. In: D'Errico, F., Poggi, I., Vinciarelli, A., Vincze, L. (eds) Conflict and Multimodal Communication. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-14081-0_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-14081-0_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14080-3

  • Online ISBN: 978-3-319-14081-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics