NDLI: Síntese de fala em português brasileiro baseada em modelos ocultos de Markov

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Síntese de fala em português brasileiro baseada em modelos ocultos de Markov

Content Provider	Semantic Scholar
Author	Souza, Carlos Francisco Soares De
Copyright Year	2010
Abstract	The technology improvement leads us to an ever closer relationship to computers and other electronic devices. Together with this progress of equipments, evolutions of humanmachine interfaces supports this not always pleasurable relationship. Therefore, the development of systems that aims to make this communication more natural and enjoyable has been in focus, and between of these, speech processing systems are excellent choices, since they allow the interaction with this devices through speech, as in traditional human communication. Currently, both the speech recognition and the synthesis are already present in everyday life, as recognizing words at the pre-services of companies' callcenters, or synthetic speech to communicate with user in GPS navigation equipment, voice readers of mobile text messages and email, among others. The use of hidden Markov models in speech processing have achieved excellent results primarily in recognition, where were his first applications and had the main highlight, and now also in synthesis, surpassing even the negative aspects of other approaches, such as need for an extensive database of phonetic units for good results. This dissertation presents development of a speech synthesizer to brazilian portuguese language, based on hidden Markov models. This works includes the construction of an algorithm for words stressed syllable determination, an algorithm for graphemes to phonemes conversion, and an algorithm to separate syllables of phonetically transcribed words. Therefore, it presents the approach characteristics and it applications in speech synthesis.
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://repositorio.ufpe.br/bitstream/123456789/2267/1/arquivo2336_1.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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