skip to main content
10.1145/1099554.1099684acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
Article

Automatic analysis of call-center conversations

Published: 31 October 2005 Publication History

Abstract

We describe a system for automating call-center analysis and monitoring. Our system integrates transcription of incoming calls with analysis of their content; for the analysis, we introduce a novel method of estimating the domain-specific importance of conversation fragments, based on divergence of corpus statistics. Combining this method with Information Retrieval approaches, we provide knowledge-mining tools both for the call-center agents and for administrators of the center.

References

[1]
J. Allan. Perspectives on information retrieval and speech. In Information Retrieval Techniques for Speech Applications, pages 1--10. Springer, 2002.
[2]
S. Busemann, S. Schmeier, and R. G. Arens. Message classification in the call center. In Proceedings of the sixth conference on Applied natural language processing, pages 158--165, San Francisco, CA, USA, 2000. Morgan Kaufmann Publishers Inc.
[3]
D. Carmel, E. Amitay, M. Herscovici, Y. S. Maarek, Y. Petruschka, and A. Soffer. Juru at trec 10 -- experiments with index pruning. In TREC, 2001.
[4]
D. Carmel, M. Shtalhaim, and A. Soffer. eResponder: Electronic Question Responder. In CooplS '00: Proceedings of the 7th International Conference on Cooperative Information Systems, pages 150--161, London, UK, 2000. Springer-Verlag.
[5]
J. Chu-Carroll and B. Carpenter. Vector-based natural language call routing. Comput. Linguist., 25(3):361--388, 1999.
[6]
D. Ferrucci and A. Lally. UIMA: an architectural approach to unstructured information processing in the corporate research environment. Natural Language Engineering, 10(3):476--489, 2004.
[7]
A. Kilgarriff. Comparing corpora. International Journal of Corpus Linguistics, 6(1):1--37, 2001.
[8]
B. Kingsbury, L. Mangu, G. Saon, G. Zweig, S. Axelrod, V. Goel, K. Visweswariah, and M. Picheny. Towards domain-inependent conversational speech recognition. In Eurospeech, Geneva, Switzerland, September 2003.
[9]
J. Kleinberg. Bursty and hierarchical structure in streams. In KDD '02: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 91--101, New York, NY, USA, 2002. ACM Press.
[10]
L. Kosseim, S. Beauregard, and G. Lapalme. Using information extraction and natural language generation to answer e-mail. Data & Knowledge Engineering, 38(1):85--100, 2001.
[11]
G. Leech, P. Rayson, and A. Wilson. Word Frequencies in Written and Spoken English: based on the British National Corpus. Longman, 2001.
[12]
G. Riccardi, A. Gorin, A. Ljolje, and M. Riley. A spoken language system for automated call routing. In Proc. ICASSP '97, pages 1143--1146, Munich, Germany, 1997.
[13]
B. Schiffman. Building a Resource for Evaluating the Importance of Sentences. In LREC02, Las Palmas, Spain, May-June 2002.
[14]
IBM WebSphere Voice Server. http : //www.ibm.com/software/pervasive/voice server.

Cited By

View all
  • (2024)Multimodal evaluation of customer satisfaction from voicemails using speech and language representationsDigital Signal Processing10.1016/j.dsp.2024.104820(104820)Online publication date: Oct-2024
  • (2024)An AI-Based Framework for Speech and Voice Analytics to Automatically Assess the Quality of Service ConversationsArtificial intelligence in application10.1007/978-3-658-43843-2_11(175-192)Online publication date: 11-Jul-2024
  • (2023)A review of natural language processing in contact centre automationPattern Analysis and Applications10.1007/s10044-023-01182-826:3(823-846)Online publication date: 29-Jun-2023
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CIKM '05: Proceedings of the 14th ACM international conference on Information and knowledge management
October 2005
854 pages
ISBN:1595931406
DOI:10.1145/1099554
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 October 2005

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. automatic speech recognition
  2. call centers

Qualifiers

  • Article

Conference

CIKM05
Sponsor:
CIKM05: Conference on Information and Knowledge Management
October 31 - November 5, 2005
Bremen, Germany

Acceptance Rates

CIKM '05 Paper Acceptance Rate 77 of 425 submissions, 18%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

CIKM '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)31
  • Downloads (Last 6 weeks)5
Reflects downloads up to 06 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Multimodal evaluation of customer satisfaction from voicemails using speech and language representationsDigital Signal Processing10.1016/j.dsp.2024.104820(104820)Online publication date: Oct-2024
  • (2024)An AI-Based Framework for Speech and Voice Analytics to Automatically Assess the Quality of Service ConversationsArtificial intelligence in application10.1007/978-3-658-43843-2_11(175-192)Online publication date: 11-Jul-2024
  • (2023)A review of natural language processing in contact centre automationPattern Analysis and Applications10.1007/s10044-023-01182-826:3(823-846)Online publication date: 29-Jun-2023
  • (2021)Development of Speech Recognition Systems in Emergency Call CentersSymmetry10.3390/sym1304063413:4(634)Online publication date: 9-Apr-2021
  • (2021)Spoken Conversational Context Improves Query Auto-completion in Web SearchACM Transactions on Information Systems10.1145/344787539:3(1-32)Online publication date: 5-May-2021
  • (2021)Data, measurement, and causal inferences in machine learning: opportunities and challenges for marketingJournal of Marketing Theory and Practice10.1080/10696679.2020.1860683(1-13)Online publication date: 11-Jan-2021
  • (2021)An approach for reducing pitch induced mismatches to detect keywords in children’s speechMultimedia Tools and Applications10.1007/s11042-021-11243-x81:19(27057-27071)Online publication date: 16-Sep-2021
  • (2020)Agent Productivity Modeling in a Call Center Domain Using Attentive Convolutional Neural NetworksSensors10.3390/s2019548920:19(5489)Online publication date: 25-Sep-2020
  • (2019)Towards Identifying Impacted Users in Cellular ServicesProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330711(3029-3039)Online publication date: 25-Jul-2019
  • (2016)Call center performance evaluation using big data analytics2016 International Symposium on Networks, Computers and Communications (ISNCC)10.1109/ISNCC.2016.7746116(1-6)Online publication date: May-2016
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media