Prosody Generation for Speech-to-Speech Translation

This paper deals with speech synthesis in the framework of speech-to-speech translation. Our current focus is to translate speeches or conversations between humans so that a third person can listen to them in its own language. In this framework the style is not written but spoken and the original speech includes a lot of non-linguistic information (as speaker emotion). In this work we propose the use of prosodic features in the original speech to produce prosody in the target language. Relevant features are found using an unsupervised clustering algorithm that finds, in a bilingual speech corpus, intonation clusters in the source speech which are relevant in the target speech. Preliminary results already show a significant improvement in the synthetic quality (from MOS=3.40 to MOS=3.65)