Please use this identifier to cite or link to this item: http://monografias.ufrn.br/handle/123456789/6730
Title: An Investigation of Type-1 Adaptive Neural Fuzzy Inference System for Speech Reconigtion
Other Titles: An Investigation of Type-1 Adaptive Neural Fuzzy Inference System for Speech Reconigtion
Authors: Lima, Thales A.
Keywords: Computação;Computing;ANFIS;ANFIS;Reconhecimento de voz;Speech Recognition;Fonemas;Phoneme;Conjuntos difusos;Fuzzy sets;Redes Neurais;Neural Networks
Issue Date: 19-Jun-2018
Publisher: Universidade Federal do Rio Grande do Norte
Citation: LIMA, Thales Aguiar de. An Investigation of Type-1 Adaptive Neural Fuzzy Inference System for Speech Reconigtion. 2018. 49 f. TCC (Graduação) - Curso de Ciência da Computação, Universidade Federal do Rio Grande do Norte, Natal, 2018.
Portuguese Abstract: Using voice for user recognition is something that humans do since the beginning and it a very natural ability. Being able to recognise the user by its voice is very important, but, in some cases, being able to recognise what is being said automatically can have very interesting and useful security applications. Thus, speech recognition has been experiencing a increasingly growth in attention in the last years, following the advancements of the machine learning field. Since this is a very complex problem and can have interference from several different sources, there has been widely different approaches to perform this task, very often with high cost, and more frequent than not, with results that are dependent on the high quality of the data, which is not always the case. In this paper we present an Type-1 Adaptive Neural Fuzzy Inference System for speech recognition on MOCHA-TIMIT repository. Besides, we also used the Mel-Frequency Cepstrum Cofficient and Filter-Banks feature extraction methods aiming to translate speech to text with low or medium quality samples and still have a good results when dealing with speech recognition.
Abstract: Using voice for user recognition is something that humans do since the beginning and it a very natural ability. Being able to recognise the user by its voice is very important, but, in some cases, being able to recognise what is being said automatically can have very interesting and useful security applications. Thus, speech recognition has been experiencing a increasingly growth in attention in the last years, following the advancements of the machine learning field. Since this is a very complex problem and can have interference from several different sources, there has been widely different approaches to perform this task, very often with high cost, and more frequent than not, with results that are dependent on the high quality of the data, which is not always the case. In this paper we present an Type-1 Adaptive Neural Fuzzy Inference System for speech recognition on MOCHA-TIMIT repository. Besides, we also used the Mel-Frequency Cepstrum Cofficient and Filter-Banks feature extraction methods aiming to translate speech to text with low or medium quality samples and still have a good results when dealing with speech recognition.
URI: http://monografias.ufrn.br/jspui/handle/123456789/6730
Other Identifiers: 20170008321
Appears in Collections:Ciência da Computação

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