Call centers handle customer queries from various domains such as computer sales and support , mobile phones , car rental , etc . Each such domain generally has a domain model which is essential to handle customer complaints . These models contain common problem categories , typical customer issues and their solutions , greeting styles . Currently these models are manually created over time . Towards this , we propose an unsupervised technique to generate domain models automatically from call transcriptions . We use a state of the art Automatic Speech Recognition system to transcribe the calls between agents and customers , which still results in high word error rates ( 40 % ) and show that even from these noisy transcriptions of calls we can automatically build a domain model . The domain model is comprised of primarily a topic taxonomy where every node is characterized by topic(s ) , typical Questions-Answers ( Q&As ) , typical actions and call statistics . We show how such a domain model can be used for topic identification of unseen calls . We also propose applications for aiding agents while handling calls and for agent monitoring based on the domain model . Call center is a general term for help desks , information lines and customer service centers . Many companies today operate call centers to handle customer issues . It includes dialog-based ( both voice and online chat ) and email support a user receives from a professional agent . Call centers have become a central focus of most companies as they allow them to be in direct contact with their customers to solve product-related and servicesrelated issues and also for grievance redress . A typical call center agent handles over a hundred calls in a day . Gigabytes of data is produced every day in the form of speech audio , speech transcripts , email , etc . This data is valuable for doing analysis at many levels , e.g. , to obtain statistics about the type of problems and issues associated with different products and services . This data can also be used to evaluate agents and train them to improve their performance . Today 's call centers handle a wide variety of domains such as computer sales and support , mobile phones and apparels . To analyze the calls in any domain , analysts need to identify the key issues in the domain . Further , there may be variations within a domain , say mobile phones , based on the service providers . The analysts generate a domain model through inspection of the call records ( audio , transcripts and emails ) . Such a model can include a listing of the call categories , types of problems solved in each category , listing of the customer issues , typical questions-answers , appropriate call opening and closing styles , etc . In essence , these models provide a structured view of the domain . Manually building such models for various domains may become prohibitively resource intensive . Another important point to note is that these models are dynamic in nature and change over time . As a new version of a mobile phone is introduced , software is launched in a country , a sudden attack of a virus , the model may need to be refined . Hence , an automated way of creating and maintaining such a model is important . In this paper , we have tried to formalize the essential aspects of a domain model . It comprises of primarily a topic taxonomy where every node is characterized by topic(s ) , typical Questions-Answers ( Q&As ) , typical actions and call statistics . To build the model , we first automatically transcribe the calls . Current automatic speech recognition technology for telephone calls have moderate to high word error rates ( Padmanabhan et al . , 2002 ) . We applied various feature engineering techniques to combat the noise introduced by the speech recognition system and applied text clustering techniques to group topically similar calls together . Using clustering at different granularity and identifying the relationship between groups at different granularity we generate a taxonomy of call types . This taxonomy is augmented with various meta information related to each node as mentioned above . Such a model can be used for identification of topics of unseen calls . Towards this , we envision an aiding tool for agents to increase agent effectiveness and an administrative tool for agent appraisal and training . Organization of the paper : We start by describing related work in relevant areas . Section 3 talks about the call center dataset and the speech recognition system used . The following section contains the definition and describes an unsupervised mechanism for building a topical model from automatically transcribed calls . Section 5 demonstrates the usability of such a topical model and proposes possible applications . Section 6 concludes the paper . We have shown that it is possible to retrieve useful information from noisy transcriptions of call center voice conversations . We have shown that the extracted information can be put in the form of a model that succinctly captures the domain and provides a comprehensive view of it . We briefly showed through experiments that this model is an accurate description of the domain . We have also suggested useful scenarios where the model can be used to aid and improve call center performance . A call center handles several hundred-thousand calls per year in various domains . It is very difficult to monitor the performance based on manual processing of the calls . The framework presented in this paper , allows a large part of this work to be automated . A domain specific model that is automatically learnt and updated based on the voice conversations allows the call center to identify problem areas quickly and allocate resources more effectively . In future we would like to semantically cluster the topic specific information so that redundant topics are eliminated from the list . We can use Automatic Taxonomy Generation(ATG ) algorithms for document summarization ( Kummamuru et al . , 2004 ) to build topic taxonomies . We would also like to link our model to technical manuals , catalogs , etc . already available on the different topics in the given domain .

Challenge: Domain models containing problem categories, typical issues, and greeting styles are manually constructed through inspection of call records to reduce the workload at call centers.
Approach: They propose an unsupervised technique to generate domain models automatically from call transcriptions obtained from an Automatic Speech Recognition system and clustering.
Outcome: They show that while the automatically obtained transcriptions are noisy, the proposed method can successfully build a domain model to provide a comprehensive view.