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Assamese is a morphologically rich, agglutinative and relatively free word order Indic language. Although spoken by nearly 30 million people, very little computational linguistic work has been done for this language. In this paper, we present our work on part of speech (POS) tagging for Assamese using the well-known Hidden Markov Model. Since no well-defined suitable tagset was available, we develop a tagset of 172 tags in consultation with experts in linguistics. For successful tagging, we examine relevant linguistic issues in Assamese.For unknown words, we perform simple morphological analysis to determine probable tags. Using a manually tagged corpus of about 10000 words for training, we obtain a tagging accuracy of nearly 87% for test inputs.

Language analysis is very important for the native speaker to connect with the digital world. Assamese is a relatively unexplored language. In this report, we analyze different aspects of speech-to-text processing, starting from building a speech corpus, defining syllable rules, and finally developing a speech search engine of Assamese. We have collected about 20 hours of speech in three (viz., read, extempore, and conversation) modes and transcribed it. We also discuss some issues and challenges faced during development of the corpus. We have developed an automatic syllabification model with 11 rules for the Assamese language and found an accuracy of more than 95% in our result. We found 12 different syllable patterns where 5 are found most frequent. The maximum length of a syllable found is four letters. With the help of Hidden Markov Model Toolkit (HTK) 3.5, we used deep learning based neural network for our speech recognition model, where we obtained 78.05% accuracy for automatic transcription of Assamese speech.

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