Skripsi

PENGENALAN SUARA KE TEKS MENGGUNAKAN HIDDEN MARKOV MODEL

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Penilaian

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Learning new words can be difficult, it helps if it’s possible to look up the new word in a dictionary, however, a word in English often sounds alike to another word, therefore a speech to text system can help searching a word in dictionary. The use of Mel-Frequency Cepstral Coefficient in feature extraction and Hidden Markov Model in recognizing speech to text was chosen because MFCC and Hidden Markov Model has better performance compared to other speech recognition machine learning methods thus in this research, an application for text to speech was developed using the combination of MFCC and Hidden Markov Model method. There are 3 HMM model that were developed by using 3 different datasets and using same configuration. The best model acquired 100% accuracy which came from second dataset that has strong and stable voice signal, clear pronunciation, and not lot of testing data.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307001566	T95623	T956232023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T956232023

Publisher

Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xv, 52 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

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Carrier Type

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Edition

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Subject(s)

Prodi Teknik Informatika
Pemrosesan Data Elektronik

Specific Detail Info

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Statement of Responsibility

MURZ

Other version/related

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