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.