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Abstract-The usage of native language through Internet is highly demanding now a day due to rapidly increase of Internet based application in daily needs. It is important to read all information in Bangla from the internet. Universal Networking Language (UNL) addressed this issue in most of languages. It helps to overcome the language barrier among people of different nations to solve problems emerging from current globalization trends and geopolitical interdependence. In this paper we propose a work that aims to contribute with morphological analysis of those Bangla words from which we obtain roots and Primary suffixes and developing of grammatical attributes for roots and Primary suffixes that can be used to prepare Bangla word dictionary and Enconversion/Deconversion rules for Natural Language Processing(NLP).

This paper presents a distinctive phonetic features (DPFs) based phoneme recognition method by incorporating syllable language models (LMs). The method comprises three stages. The first stage extracts three DPF vectors of 15 dimensions each from local features (LFs) of an input speech signal using three multilayer neural networks (MLNs). The second stage incorporates an Inhibition/Enhancement (In/En) network to obtain more categorical DPF movement and decorrelates the DPF vectors using the Gram-Schmidt orthogonalization procedure. Then, the third stage embeds acoustic models (AMs) and LMs of syllable-based subwords to output more precise phoneme strings. From the experiments, it is observed that the proposed method provides a higher phoneme correct rate as well as a tremendous improvement of phoneme accuracy. Moreover, it shows higher phoneme recognition performance at fewer mixture components in hidden Markov models (HMMs).