Mestrado em Informática

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http://repositorio.ufes.br/handle/10/17627

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Navegando Mestrado em Informática por Assunto "Acoustic modeling"

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    Automatic speech recognition, with large vocabulary, robustness, independence of speaker and multilingual processing
    (Universidade Federal do Espírito Santo, 2010-08-27) Caon, Daniel Régis Sarmento; Andreão, Rodrigo Varejão; Rauber, Thomas Walter; Varejão, Flávio Miguel; Ynoguti, Carlos Alberto
    This work aims to provide automatic cognitive assistance via speech interface, to the elderly who live alone, at risk situation. Distress expressions and voice commands are part of the target vocabulary for speech recognition. Throughout the work, the large vocabulary continuous speech recognition system Julius is used in conjunction with the Hidden Markov Model Toolkit (HTK). The system Julius has its main features described, including its modification. This modification is part of the contribution which is in this work, including the detection of distress expressions ( situations of speech which suggest emergency). Four different languages were provided as target for recognition: French, Dutch, Spanish and English. In this same sequence of languages (determined by data availability and the local of scenarios for the integration of systems) theoretical studies and experiments were conducted to solve the need of working with each new configuration. This work includes studies of the French and Dutch languages. Initial experiments (in French) were made with adaptation of hidden Markov models and were analyzed by cross validation. In order to perform a new demonstration in Dutch, acoustic and language models were built and the system was integrated with other auxiliary modules (such as voice activity detector and the dialogue system). Results of speech recognition after acoustic adaptation to a specific speaker (and the creation of language models for a specific scenario to demonstrate the system) showed 86.39 % accuracy rate of sentence for the Dutch acoustic models. The same data shows 94.44 % semantical accuracy rate of sentence.