A Metric to Classify Style of Spoken Speech
This addresses the need for automated, unbiased classification of speech styles in BPOs, reducing reliance on human analysts amid high attrition rates.
The paper tackles the problem of classifying spoken speech styles, such as American or British accents, for BPOs by proposing a new metric that robustly and accurately performs this task. Experimental results show that the system using this metric outperforms two human experts in classification accuracy.
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.