A Metric to Classify Style of Spoken Speech

TL;DR

This paper introduces a new metric for classifying spoken speech style, which outperforms human experts in accuracy, aiding BPOs in identifying speakers with specific accents efficiently.

Contribution

The paper presents a novel, robust metric for classifying speech style that reduces human bias and improves accuracy in BPO applications.

Findings

The metric outperforms two human experts in classification accuracy.

The system was tested on speech data from over seventy BPO employees.

Experimental results demonstrate the metric's robustness and reliability.

Abstract

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.