Automatic identification of gender & accent in spoken Hindi utterances with regional Indian accents

In the past significant effort has been focused on automatic extraction of information from speech signals. Most techniques have aimed at automatic speech recognition or speaker identification. Automatic accent identification (AID) has received far less attention. This paper gives an approach to identify gender and accent of a speaker using Gaussian mixture modeling technique. The proposed approach is text independent and identifies accent among four regional Indian accents in spoken Hindi and also identifies the gender. The accents worked upon are Kashmiri, Manipuri, Bengali and neutral Hindi. The Gaussian mixture model (GMM) approach precludes the need of speech segmentation for training and makes the implementation of the system very simple. When gender dependent GMMs are used, the accent identification score is enhanced and gender is also correctly recognized. The results show that the GMMs lend themselves to accent and gender identification task very well. In this approach spectral features have been incorporated in the form of mel frequency cepstral coefficients (MFCC). The approach has a wide scope of expansion to incorporate other regional accents in a very simple way.