Part of Speech Tagging of Marathi Text Using Trigram Method

In this paper we present a Marathi part of speech tagger. It is a morphologically rich language. It is spoken by the native people of Maharashtra. The general approach used for development of tagger is statistical using trigram Method. The main concept of trigram is to explore the most likely POS for a token based on given information of previous two tags by calculating probabilities to determine which is the best sequence of a tag. In this paper we show the development of the tagger. Moreover we have also shown the evaluation done

Design of English-Hindi Translation Memory for Efficient Translation

Developing parallel corpora is an important and a difficult activity for Machine Translation. This requires manual annotation by Human Translators. Translating same text again is a useless activity. There are tools available to implement this for European Languages, but no such tool is available for Indian Languages. In this paper we present a tool for Indian Languages which not only provides automatic translations of the previously available translation but also provides multiple translations, in cases where a sentence has multiple translations, in ranked list of suggestive translations for a sentence. Moreover this tool also lets translators have global and local saving options of their work, so that they may share it with others, which further lightens the task.Comment: Proceedings of National Conference in Recent Advances in Computer Engineering, 201

A Context-based Numeral Reading Technique for Text to Speech Systems

This paper presents a novel technique for context based numeral reading in Indian language text to speech systems. The model uses a set of rules to determine the context of the numeral pronunciation and is being integrated with the waveform concatenation technique to produce speech out of the input text in Indian languages. For this purpose, the three Indian languages Odia, Hindi and Bengali are considered. To analyze the performance of the proposed technique, a set of experiments are performed considering different context of numeral pronunciations and the results are compared with existing syllable-based technique. The results obtained from different experiments shows the effectiveness of the proposed technique in producing intelligible speech out of the entered text utterances compared to the existing technique even with very less storage and execution time

Marathi Speech Synthesis: A Review

This paper seeks to reveal the various aspects of Marathi Speech synthesis. This paper has reviewed research development in the International languages as well as Indian languages and then centering on the development in Marathi languages with regard to other Indian languages. It is anticipated that this work will serve to explore more in Marathi language. DOI: 10.17762/ijritcc2321-8169.15064

PROSODY PREDICTION FOR TAMIL TEXT-TO-SPEECH SYNTHESIZER USING SENTIMENT ANALYSIS

A speech synthesizer which sounds similar to a human voice is preferred over a robotic voice, and hence to increase the naturalness of a speech synthesizer an efficacious prosody model is imperative. Hence, this paper is focused on developing a prosody prediction model using sentiment analysis for a Tamil speech synthesizer. Two variations of prosody prediction models using SentiWordNet are experimented: one without a stemmer and the other with a stemmer. The prosody prediction model with a stemmer performs much more efficiently than the one without a stemmer as it tackles the highly agglutinative and inflectional words in Tamil language in a better way and is exemplified clearly, in this paper. The performance of the prosody prediction model with a stemmer has a higher classification accuracy of 77% on the test set in comparison to the 57% accuracy by the prosody model without a stemmer.Â

Nlp Challenges for Machine Translation from English to Indian Languages

This Natural Langauge processing is carried particularly on English-Kannada/Telugu. Kannada is a language of India. The Kannada language has a classification of Dravidian, Southern, Tamil-Kannada, and Kannada. Regions Spoken: Kannada is also spoken in Karnataka, Andhra Pradesh, Tamil Nadu, and Maharashtra. Population: The total population of people who speak Kannada is 35,346,000, as of 1997. Alternate Name: Other names for Kannada are Kanarese, Canarese, Banglori, and Madrassi. Dialects: Some dialects of Kannada are Bijapur, Jeinu Kuruba, and Aine Kuruba. There are about 20 dialects and Badaga may be one. Kannada is the state language of Karnataka. About 9,000,000 people speak Kannada as a second language. The literacy rate for people who speak Kannada as a first language is about 60%, which is the same for those who speak Kannada as a second language (in India). Kannada was used in the Bible from 1831-2000. Statistical machine translation (SMT) is a machine translation paradigm where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The statistical approach contrasts with the rule-based approaches to machine translation as well as with example-based machine translatio