A Metric to Classify Style of Spoken Speech

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.

Knowledge driven Offline to Online Script Conversion

The problem of offline to online script conversion is a challenging and an ill-posed problem. The interest in offline to online conversion exists because there are a plethora of robust algorithms in online script literature which can not be used on offline scripts. In this paper, we propose a method, based on heuristics, to extract online script information from offline bitmap image. We show the performance of the proposed method on a real sample signature offline image, whose online information is known.