REAL TIME SPEECH TO SPEECH TRANSLATION

In this paper, we have studied on real-time speech-to-speech translation model for Amharic and Afaan Oromo languages. The model studied has three basic components such speech recognition, machine translation, and speech synthesis. For the speech recognition section, we have used HMM, a Hybrid approach for MT, and a concatenative synthesizer for TTS translation. HTK for speech recognition, IRSTLM for language modeling, GIZA++ for word-level alignment, MOSES as decoder, and Festvox tool for speech synthesis are used as a toolkit. In our evaluation we have found the ASR mode with an accuracy of 89.21%, the MT module shows 90% accuracy for Amharic to Afaan Oromo translation and 88.4% Accuracy for Afaan Oromo to Amharic translation and, TTS synthesizer scores 3 out of 5 on average after it is evaluated by three individuals. Keyword: machine translation, speech recognition, speech synthesis, language model.