|David Sündermann received his M.Sc. in EE (with Distinction) from the Dresden University of Technology in 2002 and started his PhD project at the RWTH Aachen (Germany) with Hermann Ney. In 2003, he received a PhD Fellowship by Siemens Corporate Technology in Munich (Germany) and relocated to Barcelona (Spain) in 2004, working as research staff member at the Technical University of Catalonia with Antonio Bonafonte. In 2005, he was visiting scientist at the University of Southern California in LA (USA) with Shri Narayanan and, later this year, at the Columbia University in NYC (USA) with Julia Hirschberg. He has written more than 25 papers and holds a patent on voice conversion.|
For applications like multi-user speech-to-speech translation, it is helpful to individualize the output voice to make voices distinguishable. Ideally, this should be done by applying the input speaker's voice characteristics to the output speech.
In general, a speech-to-speech translation system consists of three main modules: speech recognition, text translation, and speech synthesis.
Since the latter, the speech synthesis module, normally is based on a large speech corpus of a professional speaker manually corrected and carefully tuned, the output voice characteristics are static. This is overcome by a fourth module, the voice conversion unit, which processes the synthesizer's speech according to the input voice characteristics.
Due to the nature of speech-to-speech translation, input and output voices use different languages leading to the following two challenges:
In this talk, I present text-independent voice conversion techniques that are cross-language portable and aim at solving these challenges. In this context, I will
The techniques' performance is assessed on several multi-lingual corpora in the framework of subjective evaluations. In addition to the evaluation results, speech samples will be used to illustrate the discussed techniques' effectiveness.