• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Deep learning in automatic dubbing

Student: Yurij Golubiczkij

Supervisor: Maxim Kaledin

Faculty: Faculty of Computer Science

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2024

This project delves into the application of deep learning methodologies in the field of automatic dubbing. The main goal of this initiative is to delve deeper into the application of neural networks to optimize duplication procedures. The project aims to solve problems inherent to content creators working in various multilingual environments. Using the power of artificial intelligence and deep learning algorithms, the project aims to pave the way for automated dubbing processes. Through research into different neural network models, the initiative aims to provide practical solutions to the challenges that arise when creating this type of content.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses