  The ability to classify spoken speech based on the style of speaking is an
important problem. With the advent of BPO's in recent times, specifically those
that cater to a population other than the local population, it has become
necessary for BPO's to identify people with certain style of speaking
(American, British etc). Today BPO's employ accent analysts to identify people
having the required style of speaking. This process while involving human bias,
it is becoming increasingly infeasible because of the high attrition rate in
the BPO industry. In this paper, we propose a new metric, which robustly and
accurately helps classify spoken speech based on the style of speaking. The
role of the proposed metric is substantiated by using it to classify real
speech data collected from over seventy different people working in a BPO. We
compare the performance of the metric against human experts who independently
carried out the classification process. Experimental results show that the
performance of the system using the novel metric performs better than two
different human expert.
