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Navegando por Assunto "Hidden Markov models"

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    Conversão grafema-fone para um sistema de reconhecimento de voz com suporte a grandes vocabulários para o português brasileiro
    (Universidade Federal do Pará, 2006-06-12) HOSN, Chadia Nadim Aboul; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
    Speech processing has become a data-driven technology. Hence, the success of research in this area is linked to the existence of public corpora and associated resources, as a phonetic dictionary. In contrast to other languages such as English, one cannot find, in public domain, a Large Vocabulary Continuos Speech Recognition (LVCSR) System for Brazilian Portuguese. This work discusses some efforts within the FalaBrasil initiative [1], developed by researchers, teachers and students of the Signal Processing Laboratory (LaPS) at UFPA, providing an overview of the research and softwares related to Automatic Speech Recognition (ASR) for Brazilian Portuguese. More specifically, the present work discusses the implementation of a large vocabulary ASR for Brazilian Portuguese using the HTK software, which is based on hidden Markov models (HMM). Besides, the work discusses the implementation of a grapheme-phoneme conversion module using machine learning techniques.
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    Desenvolvimento de recursos para a construção de um sistema texto-fala para o português brasileiro
    (Universidade Federal do Pará, 2010-12-23) COUTO, Igor Costa do; KLAUTAU JÚNIOR, Aldebaro Barreto da Rocha; http://lattes.cnpq.br/1596629769697284
    Text-to-speech (TTS) is currently a mature technology that is used in many applications. Some modules of a TTS depend on the language and, while there are many public resources for English, the resources for some underrepresented languages are still limited. This work describes the development of a complete TTS system for Brazilian Portuguese (BP) which expands the already available resources. The system uses the MARY framework and is based on the hidden Markov model (HMM) speech synthesis approach. Some of the contributions of this work consist in implementing syllabification, determination of stressed syllable and grapheme-tophoneme (G2P) conversion. This work also describes the steps for organizing the developed resources and implementing a BP voice within the MARY. These resources are made available and facilitate the research in text normalization and HMM-based synthesis for BP.
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