Voice based call centers enable customers query
for information by speaking to human agents. Most
often these call conversations are recorded by call centers
with the intent of trying to identify things that
can help improve the performance of the call center
to serve the customer better. Today the recorded
conversations are analyzed by humans by listening
to call conversations, which is both time consuming,
fatigue prone and not very accurate. Additionally,
humans are able to analyze only a small percentage
of the total calls because of economics. In this paper
which is based on [1], we propose a visual method to
identify problem calls quickly. The idea is to sieve
through all the calls and identify problem calls, these
calls can then be further analyzed by human. We first
model call conversations as a directed graph and then
identify a directed graph structure associated with a
normal call. All call conversations that do not have
the structure of a normal call are then classified as
being abnormal. We use the speaking rate feature to
model call conversation because it can spot potential
problem calls. We have experimented on real call center
conversations acquired from different call centers
and the results are encouraging.