Year: 2025
Type: article
Abstract: Deep learning in speech synthesis systems.Deep learning systems allow you to automate complex tasks that previously required human intelligence, and do so with high accuracy.Deep learning uses artificial neural networks with many layers -each layer processes information in an increasingly complex and abstract way.This allows the system to learn high-level features such as emotions, intonations, expressiveness, etc.The introduction of these features makes synthesized speech more natural, which contributes to its better perception by humans.Unlike traditional speech synthesis methods, such as formant synthesis, concatenative synthesis or HMM-based approaches (Hidden Markov Models), deep learning provides much higher flexibility and sound quality.In traditional systems, speech was built from pre-recorded fragments or generated according to predefined rules, which limited the naturalness, intonation richness and emotional coloring of the voice.Thus, deep learning overcomes key limitations of traditional approaches and opens up new opportunities in the field of voice technologiesfrom text-to-speech to full-fledged emotional communication between humans and machines.The article considers the main areas of application of deep learning for speech synthesis, analyzes existing approaches to building synthesis systems, and analyzes their weaknesses and strengths.
Authors: С. М. Іщеряков, А. О. Попов
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4412845767