This thesis introduces a general method for incorporating the distributional analysis
of textual and linguistic objects into text-to-speech (TTS) conversion systems.
Conventional TTS conversion uses intermediate layers of representation to bridge
the gap between text and speech. Collecting the annotated data needed to produce
these intermediate layers is a far from trivial task, possibly prohibitively so
for languages in which no such resources are in existence. Distributional analysis,
in contrast, proceeds in an unsupervised manner, and so enables the creation of
systems using textual data that are not annotated. The method therefore aids
the building of systems for languages in which conventional linguistic resources
are scarce, but is not restricted to these languages.
The distributional analysis proposed here places the textual objects analysed
in a continuous-valued space, rather than specifying a hard categorisation of those
objects. This space is then partitioned during the training of acoustic models for
synthesis, so that the models generalise over objects' surface forms in a way that
is acoustically relevant.
The method is applied to three levels of textual analysis: to the characterisation
of sub-syllabic units, word units and utterances. Entire systems for three
languages (English, Finnish and Romanian) are built with no reliance on manually
labelled data or language-specific expertise. Results of a subjective evaluation
are presented