Unsupervised and lightly-supervised learning for rapid construction of TTS systems in multiple languages from `found' data: evaluation and analysis

This paper presents techniques for building text-to-speech front-ends in a way that avoids the need for language-specific expert knowledge, but instead relies on universal resources (such as the Unicode character database) and unsupervised learning from unannotated data to ease system development. The acquisition of expert language-specific knowledge and expert annotated data is a major bottleneck in the development of corpus-based TTS systems in new languages. The methods presented here side-step the need for such resources as pronunciation lexicons, phonetic feature sets, part of speech tagged data, etc. The paper explains how the techniques introduced are applied to the 14 languages of a corpus of `found' audiobook data. Results of an evaluation of the intelligibility of the systems resulting from applying these novel techniques are presented.