Data Mining And Its Application In The Chinese Text To Speech

To meet the requirement, Knowledge Discovery in Database(KDD) comes into being these years. This thesis have done research on the KDD process model, and finding prosodic rules for Mandarin Speech synthesis by data mining. The results are encouraging.KDD has been caught more and more attention recently, but most of the current research on KDD pay much attention to data mining, which is one stage of KDD, and little to the KDD process model. But actually data mining has been done little amount of mining work of the whole discovery task. Reasonable KDD process model can organize the whole discovery stages into an solid unit, and thus makes it easy for end users to use KDD.We propose a KDD process model which is based on the analysis of the practice, and it supports dataset and multi-thread training. The proposed model is more suitable for the KDD application, and it makes the influence between data mining expert and end user as little as possible, so it can make knowledge discovery more efficient.The current synthesized speech has low quality, and one of the reasons is that the prosodic rules which are now being used are unsatisfied. We propose to learn prosodic models by data mining from actual speech database, and it was implemented in the learning from phrases and sentences.To extract pitch variation patterns from two-word phrases, a data mining system called SpeechDM has been implemented. In Chinese, the pitches extracted from an isolate syllable differ from those extracted from the same syllable in phrases, and SpeechDM extracts the patterns from the mapping between them. Since the pitch variation patterns have been learned from actual speech, it is possible to improve the naturalness of synthesized speech.The pitch models which are now being used in Mandarin Text-To-Speech are extracted by linguistics experts, and they are described qualitatively and with low precise. To acquire more accurate prosodic rules, data mining is...