From each of the translated Malayalam text, syllables are separated. When an English text is given, it is translated to Malayalam with the help of a parser, using grammatical rules, applying morphology and a bilingual dictionary. It is achieved by combining both Machine Translation and TTS. This works aims to help people to translate English text to their own language and implement a TTS for the minority language, Malayalam. It becomes difficult to understand the English and the other European languages to common people. India has the largest democracy system in the world, also known as land of unity in diversity and has more than 22 official languages. Research is conducting all over the world, to develop an efficient text to speech synthesis (TTS) system for minority languages. Speech recognition is a system which generates text for the given speech, while a speech synthesizer is a system that should be able to read any text aloud. Speech recognition and Speech synthesis are the two emerging technologies in the communication field. Experiments conducted on a sample corpus have generated reasonably good Malayalam translations and the results are verified with F measure, BLEU and WER evaluation metrics The handcrafted rules designed for the suffix separation process which can be used as a guideline in implementing suffix separation in Malayalam language are also presented in this paper. Difficulties in translation process that arise due to the structural difference between the English Malayalam pair is resolved in the decoding phase by applying the order conversion rules. Pre-processing techniques like suffix separation from the Malayalam corpus and stop word elimination from the bilingual corpus also proved to be effective in producing better alignments. Removing the insignificant alignments from the sentence pairs by this approach has ensured better training results. Various techniques to improve the alignment model by incorporating the morphological inputs into the bilingual corpus are discussed. The translator utilizes a monolingual Malayalam corpus and a bilingual English/Malayalam corpus in the training phase and generates automatically the Malayalam translation of an unseen English sentence. In future, this application aims to achieve ubiquitous communication enabling people of different regions to communicate with each other breaking the language barriers.Ī methodology for translating text from English into the Dravidian language, Malayalam using statistical models is discussed in this paper. This application is beneficial to visually impaired people as well as individuals who are not capable of reading text to acquire knowledge in their native language. This accuracy is verified by comparing naturality of the audio with the general spoken language. The accuracy of the application developed can be as high as 91 percent for a single video and averages about 79 percent. This flite engine is used as TTS for generating audio from translated text. Alan Black at Carnegie Mellon University (CMU)). One such available application is the flite engine (a lighter version of Festival engine developed by Prof. Not many open source TTS systems are available for Indian regional languages. After this translation, a TTS system is required to convert the text into the desired audio output. The obtained text chunks are then concatenated and passed to Google's machine translate API for conversion to the requested Indian language. These audio chunks are then individually converted into text using IBM Watson's speech-to-text (STT) module.

It then divides the audio file into several smaller chunks based on the timestamps. This application takes an English language video as an input and separates the audio from video. This paper aims to leverage the open source applications of these technologies, machine translation, text-to-speech system (TTS), and speech-to-text system (STT) to convert available online resources to Indian languages. The recognition and synthesis of speech are prominent emerging technologies in natural language processing and communication domains. In spite of many languages being spoken in India, it is difficult for the people to understand foreign languages like English, Spanish, Italian, etc.

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