An evolutionary approach for accent classification in IVR systems

This paper describes a speaker-independent accent-based natural language call-routing system. Based on a speaker´s accent group, this system directs customer calls to the automatic speech recognition system that is most suitable to recognize the input query. The speech recognition system understands the caller´s query and converts it into routing keywords. Accent identification is the most important factor for improving the performance of natural language call-routing systems because accents vary widely, even within the same country or community. This variation occurs when non-native speakers start to learn a second language; the substitution of native language phoneme pronunciation is a common occurrence. In this paper, a new method is proposed based on class inequivalent side information and an evolutionary-based K-means clustering algorithm. In a distance metric learning approach, data points are transferred to a new space where the Euclidean distances between similar and dissimilar points are at their minimum and maximum, respectively. However, the evolutionary-based K-means clustering approach yields globally optimized Gaussian components for an accent classification system.