Correction of Prosodic Phrases in Large Speech Corpora

Abstract

Nowadays, in many speech processing tasks, such as speech recognition and synthesis, really large speech corpora are utilized. These speech corpora usually contain several hours of speech or even more. To achieve possibly best results, an appropriate annotation of the recorded utterances is often necessary. This paper is focused on problems related to the prosodic annotation of the Czech speech corpora. In the Czech language, the utterances are supposed to be split by pauses into so-called prosodic clauses containing one or more prosodic phrases. The types of particular phrases are linked to their last prosodic words corresponding to various functionally involved prosodemes. The clause/phrase structure is substantially determined by the sentence composition. However, in real speech data, different prosodeme type or even phrase/clause borders can be present. This paper deals with 2 basic problems: the correction of the improper prosodeme/phrase type and the detection of new phrase borders. For both tasks, we proposed new procedures utilizing hidden Markov models. Experiments were performed on 4 large speech corpora recorded by professional speakers for the purpose of speech synthesis. These experiments were limited to the declarative sentences. The results were successfully verified by listening tests.