CALL CENTERS (CENTRES) - Faculty of Industrial Engineering and ...

2003, 396–406.
Abstract. We consider a network of service-providing agents, where different agents have different
capabilities, availability, and cost to solve problems. These characteristics are particularly
important in practice for semi-automated call centers which provide quality customer service in
real time. We have developed SANet, a service agent network for call center automation, to serve
as an experimental test-bed for our research. SANet can select appropriate agents to provide
better solutions for customer problems according to the changing capabilities and availability of
service agents in the network. It can also add or delete appropriate agents to balance problemsolving
quality, efficiency, and cost according to the number and types of incoming customer
problems. On this network, each service agent can be a human service agent, an automated
software service agent, or a combination of the two. This paper describes the architecture, a
problem scheduling algorithm and an agent assignment algorithm on the SANet. We highlight
an application in which we apply SANet to a call-center scheduling problem for a cable TV company.
Finally, we show the efficiency and adaptability of our system via experimental results
and discuss related works.
Keywords: Cable television, Call centres, Multi-agent systems, Problem-solving, Real-time systems
(Appears also in Section I.)
42. Huang, Qiang and S. Cox. Improving phoneme recognition of telephone quality speech, IEEE
International Conference on Acoustics, Speech, and Signal Processing, 1, 2004, I-445–448.
Abstract. There are some speech understanding applications in which training transcriptions
are unavailable, and hence the vocabulary is unknown, but the task is to recognise key words
and phrases within an utterance rather than to attempt a complete accurate transcription. An
example of such a task is call-routing, when transcriptions of training utterances (which are very
expensive to produce) are unavailable. In such cases, phoneme rather than word recognition
is appropriate. However, phoneme recognition of spontaneous speech spoken by a large multiaccented
population over telephone connections is very inaccurate. To improve accuracy, we describe
a technique in which we segment the waveform into subword-like units and use clustering
and an iteratively refined language model to correct the errors in the recognised phonemes. The
method was shown to work well on telephone quality spontaneous speech, raising the phoneme
accuracy from 28.1% after the first iteration to 47.3% after three iterations.
43. Williams, J.D. and S.M. Witt. A comparison of dialog strategies for call routing, International
Journal of Speech Technology, 7 (1), 2004, 9–24.
Abstract. Advances in commercially-available ASR technology have enabled the deployment
of “how-may-I-help-you?” interactions to automate call routing. While often preferred to menubased
or directed dialog strategies, there is little quantitative research into the relationship
among prompt style, task completion, user preference/satisfaction, and domain. This work applies
several dialog strategies to two domains, drawing on both real callers and usability subjects.
We find that longer greetings produce higher levels of first-utterance routability. Further, we
show that a menu-based dialog strategy produces a uniformly high level of routability at the first
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