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Learning Full Pitch Variation Patterns with NeuralNets

  • Tingshao Zhu
  • Wen Gao
  • Charles X. Ling
Conference paper

Abstract

Prosodic model is very important for speech synthesis. It includes pitch model, duration model and pause model, and pitch model is the most important. Now most pitch models are constructed by linguistics experts, and they are described qualitatively and with low precise. We consider the pitch models as the mapping between the pitches of isolate syllable and those of the same one in phrase, so neural net can be used to learn the patterns. For acquiring these patterns quantitatively and precisely, BP networks are established to extract pitch and duration variation patterns from large speech database. Since the networks have been trained from actual speech samples, the quality of synthesis speech which is based on the networks can be high. In this paper, the architecture is first specified, then the new time wrapping algorithm and the networks are introduced in detail, and at last results are given too.

Keywords

Speech Data Speech Synthesis Duration Model Pitch Variation Speech Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 1999

Authors and Affiliations

  • Tingshao Zhu
    • 1
  • Wen Gao
    • 1
  • Charles X. Ling
    • 2
  1. 1.MOTOROLA-ICT Joint R&D Lab, Institute of Computing TechnologyAcademia SinicaBeijingChina
  2. 2.Department of Computer ScienceUniversity of Western OntarioLondonCanada

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