Neural Networks
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Initial phoneme is used in spoken word recognition models. These are used to activate words starting with that phoneme in spoken word recognition models. Such investigations are critical for classification of initial phoneme into a phonetic group. A work is described in this paper using an artificial neural network (ANN) based approach to recognize initial consonant phonemes of Assamese words. A self organizing map (SOM) based algorithm is developed to segment the initial phonemes from its word counterpart. Using a combination of three types of ANN structures, namely recurrent neural network (RNN), SOM and probabilistic neural network (PNN), the proposed algorithm proves its superiority over the conventional discrete wavelet transform (DWT) based phoneme segmentation. The algorithm is exclusively designed on the basis of Assamese phonemical structure which consists of certain unique features and are grouped into six distinct phoneme families. Before applying the segmentation approach using SOM, an RNN is used to take some localized decision to classify the words into six phoneme families. Next the SOM segmented phonemes are classified into individual phonemes. A two-class PNN classification is performed with clean Assamese phonemes, to recognize the segmented phonemes. The validation of recognized phonemes is checked by matching the first formant frequency of the phoneme. Formant frequency of Assamese phonemes, estimated using the pole or formant location determination from the linear prediction model of vocal tract, is used effectively as a priori knowledge in the proposed algorithm.