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020			_a9783030027599
024	7		_a10.1007/978-3-030-02759-9 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aTK7882.S65 _b2019 EB
100	1		_aRao, K. Sreenivasa _eautor _9671145 _q(Krothapalli Sreenivasa)
245	1	0	_aSource Modeling Techniques for Quality Enhancement in Statistical Parametric Speech Synthesis _cby K. Sreenivasa Rao, N. P. Narendra.
264		1	_aCham _bImprint: Springer _c2019
300			_a1 recurso en línea (XII, 136 páginas) _b74 ilustraciones, 11 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
490	0		_aSpringerBriefs in Speech Technology Studies in Speech Signal Processing Natural Language Understanding and Machine Learning _x2191-737X
505	0		_aChapter 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.
520	3		_aThis 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.
650		7	_2embne _aReconocimiento automático del lenguaje _9147323
650		7	_2embne _9666075 _aLingüística computacional
700	1		_aNarendra, N. P. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783030027582
776	0	8	_iPrinted edition: _z9783030027605
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-02759-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aPrimersemestre_2019_Engineering
998			_aSI _cm _dz _feng _ggw _h0 _b10/2019 _ek _zSI