04341nam 22006135 450 991033764920332120250609110727.03-030-02759-710.1007/978-3-030-02759-9(CKB)4100000007223653(MiAaPQ)EBC5615398(DE-He213)978-3-030-02759-9(PPN)232965102(MiAaPQ)EBC5917791(EXLCZ)99410000000722365320181213d2019 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierSource Modeling Techniques for Quality Enhancement in Statistical Parametric Speech Synthesis /by K. Sreenivasa Rao, N. P. Narendra1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (145 pages)SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,2191-737X3-030-02758-9 Includes bibliographical references and index.Chapter 1. Introduction -- Chapter 2. Background and literature review -- Chapter 3. Robust voicing detection and F0 estimation method -- Chapter 4. Parametric approach of modeling the source signal -- Chapter 5. Hybrid approach of modeling the source signal -- Chapter 6. Generation of creaky voice -- Chapter 7. Summary and conclusions.This book presents a statistical parametric speech synthesis (SPSS) framework for developing a speech synthesis system where the desired speech is generated from the parameters of vocal tract and excitation source. Throughout the book, the authors discuss novel source modeling techniques to enhance the naturalness and overall intelligibility of the SPSS system. This book provides several important methods and models for generating the excitation source parameters for enhancing the overall quality of synthesized speech. The contents of the book are useful for both researchers and system developers. For researchers, the book is useful for knowing the current state-of-the-art excitation source models for SPSS and further refining the source models to incorporate the realistic semantics present in the text. For system developers, the book is useful to integrate the sophisticated excitation source models mentioned to the latest models of mobile/smart phones. Presents the efficient excitation source modeling techniques for generating high quality speech; Includes a combination of both waveform and parametric methods to enhance the quality of synthesis; Features and methods that need less memory and computational requirements than others, allowing them to be integrated to smart phones and smaller devices.SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning,2191-737XSignal processingImage processingSpeech processing systemsNatural language processing (Computer science)Computational linguisticsSignal, Image and Speech Processinghttps://scigraph.springernature.com/ontologies/product-market-codes/T24051Natural Language Processing (NLP)https://scigraph.springernature.com/ontologies/product-market-codes/I21040Computational Linguisticshttps://scigraph.springernature.com/ontologies/product-market-codes/N22000Signal processing.Image processing.Speech processing systems.Natural language processing (Computer science)Computational linguistics.Signal, Image and Speech Processing.Natural Language Processing (NLP).Computational Linguistics.006.54Rao K. Sreenivasa(Krothapalli Sreenivasa),authttp://id.loc.gov/vocabulary/relators/aut0Narendra N. Pauthttp://id.loc.gov/vocabulary/relators/autBOOK9910337649203321Source Modeling Techniques for Quality Enhancement in Statistical Parametric Speech Synthesis4122458UNINA