Research On Modelling And Conversion Of Segmental Feature

Voice conversion is a voice signal processing technology that aims to transform the voice of a speaker (source speaker), for it to be perceived by listeners as if it had been uttered by another speaker (target speaker), while keeping the semantics and emotional information unchanged. so that the source speaker's voice sounds like the target speaker's voice. The technology not only has the important theory research value, but also has excellent commercial application value, and the scholars pay more and more attention to the current and trend research at home and abroad.The main work and contributions are described as follows:Firstly, the paper briefly introduces the application valuation and classical algorithms, and discusses the familiar identity parameters of voice and some basic principles of voice conversion.Secondly, this paper researches some classical pitch frequency transformation algorithm. The classical algorithms exist lowness of transformation precision and ropiness of synthesized speech. For this reason, the pitch frequency transformation algorithm, based on the STRAIGHT+BP Neural Network, is proposed. The new method is evaluated by means of both objective and subjective tests, the experimentaion result has proved the validity of the method. Finally, this paper proposes a new morphing algorithm based on BP Neural Network, optimized by Quantum Particle Swarm Optimization(QPSO), and addresses a study on voice conversion using QPSO to train the factors of BP neural network, which can help better capture the nonlinear mapping between different speakers. Besides, compared with standard BP Neural Network method about the performance of conversion. This method availably accomplishes spectrum conversion of voice, and carries out the speaker's conversion. The perceptual tests prove that this method advances the performance of voice conversion in some degree.