Speaker identification model for Assamese language using a neural framework

This paper presents a neural model of speaker identification using the vowel sound segmented out from words spoken by a speaker. Vowel sounds occur in a speech more frequently and with higher energy. Therefore, situations where acoustic information is noise corrupted vowel sounds can be used to extract different amounts of speaker discriminative information. The model explained here uses a neural framework formed with Probabilistic Neural Network (PNN) and Learning Vector Quantization (LVQ) where a novel Self Organizing Map (SOM) based vowel segmentation technique is used. The work extracts glottal source information of the speakers by Empirical-Mode Decomposition (EMD) of the speech signal and depending on which a LVQ based speaker code book is formed. The work shows the use of residual signal obtained from EMD of speech as a speaker discriminative feature. The neural approach of speaker identification gives superior performance in comparison to the conventional statistical approach like Hidden Markov Models (HMMs), Gaussian Mixture Models (GMMs) etc. found in literature. The work formulates a framework for the design of a ANN based speaker recognition model for Assamese language which is spoken by around three million people in the North East Indian state of Assam. Although the proposed model has been experimented in case of the speakers of Assamese language, it shall also be suitable for other Devanagari based languages for which the speaker database should contain samples of that specific language.