Automatic spoken language recognition with neural networks
Автор: Valentin Gazeau, Cihan Varol
Журнал: International Journal of Information Technology and Computer Science @ijitcs
Статья в выпуске: 8 Vol. 10, 2018 года.
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Translation has become very important in our generation as people with completely different cultures and languages are networked together through the Internet. Nowadays one can easily communicate with anyone in the world with the services of Google Translate and/or other translation applications. Humans can already recognize languages that they have priory been exposed to. Even though they might not be able to translate, they can have a good idea of what the spoken language is. This paper demonstrates how different Neural Network models can be trained to recognize different languages such as French, English, Spanish, and German. For the training dataset voice samples were choosed from Shtooka, VoxForge, and Youtube. For testing purposes, not only data from these websites, but also personally recorded voices were used. At the end, this research provides the accuracy and confidence level of multiple Neural Network architectures, Support Vector Machine and Hidden Markov Model, with the Hidden Markov Model yielding the best results reaching almost 70 percent accuracy for all languages.
Hidden Markov Model, Language Identification, Language Translation, Neural Networks, Support Vector Machine
Короткий адрес: https://sciup.org/15016284
IDR: 15016284 | DOI: 10.5815/ijitcs.2018.08.02
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