PENGENALAN SUARA KE TEKS MENGGUNAKAN GAUSSIAN MIXTURE MODEL

FATARAH, MUHAMMAD DAFFA RIZKY and Arsalan, Osvari and Kurniati, Rizki (2023) PENGENALAN SUARA KE TEKS MENGGUNAKAN GAUSSIAN MIXTURE MODEL. Undergraduate thesis, Sriwijaya University.

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Abstract

English content is very easy to find in the surrounding environment such as in social media, but Indonesians find it difficult to understand. Since English word often sounds similar to other words and also the pronunciation differences of each person, a speech-to-text recognition system can help recognize English words into text. Gaussian Mixture Model is chosen to recognize speech to text because it is better than other speech recognition machine learning methods. The stages in GMM include Lower Bound and Expectation Maximization, where the result of GMM is a Gaussian distribution with mean (μ) and variance (σ2) parameters. Therefore, a voice-to-text recognition application was developed in this research using the Gaussian Mixture Model method. Three GMM models were created using three different datasets and the same parameter configuration. The best model gives perfect results with 100% accuracy using datasets that have stable voice signals, clear pronunciation, and little test data.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Speech-to-Text, Gaussian Mixture Model, Recognize, Machine Learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Muhammad Daffa Rizky Fatarah
Date Deposited:	12 Apr 2023 02:37
Last Modified:	12 Apr 2023 02:37
URI:	http://repository.unsri.ac.id/id/eprint/95757

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