Combinatorial issues in text-to-speech synthesis

Abstract

Enhanced storage capacities and new learning algorithms have increased the role of text and speech training data bases in the construction of text-to-speech systems. It has become apparent, however, that not always learning algorithms are available that have strong generalization capabilities – the ability to generalize from cases seen in the training data base to new cases encountered during TTS operation. This makes it important to measure and understand the degree of coverage of the input domain of a text-to-speech system (usually, the entire language) by a given training data base. The goal of this paper is to investigate the feasibility of coverage in several domains of interest for TTS. It is shown that, as a result of the combinatorics of language, coverage is typically quite disappointing. This puts a premium on the generalization capability of learning algorithms.