A Study On Lhasa Tibetan Prosodic Model Of Journalese

Based on the fast development of large-scale corpus waveform joining technology, speech synthesis system studying has gained significant progress and the synthesized speech intelligibility has been able to meet the needs of practical applications. However, the naturalness of synthesized speech is still insufficiently ideal, mainly because of deficient prosodic model in synthesis system. High-quality prosodic model must to be established in order to eliminate the difference between synthesized speech and human nature language flow for higher naturalness of speech. At present, the data-driven method is much more popular than others, using lots of corpora to do model training for outputting high-quality prosodic control parameters and improving the speech synthesized naturalness.To realize the oral language flow is limited by the human physiological mechanism, mainly referring to respiratory regulation. Respiration is as an important clue for prosodic layer classification. Research on the interrelationship between respiration and prosodic layers, to confirm the respiratory signal parameters of prosodic features and regard them as training parameters, is considered as a new type of prosodic model processing and a new attempt for establishing high-quality prosodic model.In accordance with the actual development for Tibetan speech synthesis, the paper has taken news text as training corpora, analyzed the speech and prosodic features of Tibetan Lhasa dialect and confirmed the respiratory signal parameters with prosodic features, then adopted RBF neural network to establish Tibetan Lhasa news prosodic model and finally realized the predictions of prosodic control parameters. The main work includes as follows:1. Research the speech features of Tibetan Lhasa dialect combining with the previous results of Chinese prosodic structure; confirm the Tibetan prosodic layers and analyzed Tibetan prosodic structure and features; determine the parameters being able to reflect prosodic features regarded as input parameter-set for prosodic model.2. Collect the Tibetan Daily for the whole year; design and optimize the texts according to Tibetan Lhasa dialect features; make sure that all corpora has covered the Tibetan speech segments and supra-segments; design the prosodic labeling principles suitable with Tibetan features after normalizing and speech recording; establish the prosodic model corpus for Tibetan Lhasa dialect.3. Research the changing features of respiratory signals during breathing according to human physiological mechanism; confirm the corresponding relationship between respiratory signals and prosodic features after data analysis and collect the related parameters used for model training parameters.4. Confirm 6 classes of 39 dimensions context feature parameters in terms of previous prosodic structure analysis results; use RBF neural network to establish prosodic model and output 10 dimensions prosodic control parameters; make use of the labeled corpora in corpus for model training and testing to know the predictable nature of the established model.