Automatic corpus-based tone and break-index prediction using K-ToBI representation

In this article we present a prosody generation architecture based on K-ToBI (Korean Tone and Break Index) representation. ToBI is a multitier representation system based on linguistic knowledge that transcribes events in an utterance. The TTS (Text-To-Speech) system, which adopts ToBI as an intermediate representation, is known to exhibit higher flexibility, modularity, and domain/task portability compared to the direct prosody generation TTS systems. However, for practical-level performance, the cost of corpus preparation is very expensive because the ToBI labeled corpus is constructed manually by many prosody experts, and normally requires large amounts of data for statistical prosody modeling. Unlike previous ToBI-based systems, this article proposes a new method, which transcribes the K-ToBI labels in Korean speech completely automatically. We develop automatic corpus-based K-ToBI labeling tools and prediction methods based on several lexico-syntactic linguistic features for decision-tree induction. We demonstrate the performance of F0 generation from automatically predicted K-ToBI labels, and confirm that the performance is reasonably comparable to state-of-the-art direct prosody generation methods and previous ToBI-based methods.