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The ability to classify spoken speech based on the style of speaking is animportant problem. With the advent of BPO's in recent times, specifically thosethat cater to a population other than the local population, it has becomenecessary for BPO's to identify people with certain style of speaking(American, British etc). Today BPO's employ accent analysts to identify peoplehaving the required style of speaking. This process while involving human bias,it is becoming increasingly infeasible because of the high attrition rate inthe BPO industry. In this paper, we propose a new metric, which robustly andaccurately helps classify spoken speech based on the style of speaking. Therole of the proposed metric is substantiated by using it to classify realspeech data collected from over seventy different people working in a BPO. Wecompare the performance of the metric against human experts who independentlycarried out the classification process. Experimental results show that theperformance of the system using the novel metric performs better than twodifferent human expert.

A Metric to Classify Style of Spoken Speech