Skripsi

PENGENALAN SUARA KE TEKS MENGGUNAKAN GAUSSIAN MIXTURE MODEL.

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307001567	T95757	T957572023	Central Library (Referens)	Available

Detail Information

Series Title

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Call Number

T957572023

Publisher

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

Collation

xvi, 56 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.07

Content Type

Text

Media Type

unmediated

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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