Current advances and algorithmic solutions in speech generation

Abstract

Currently, Text-to-Speech (TTS) technology, aimed at reproducing a natural human voice from text, is gaining increasing demand in natural language processing. Key criteria for evaluating the quality of synthesized sound include its clarity and naturalness, which largely depend on the accurate modeling of intonations using the acoustic model in the speech generation system. This paper presents fundamental methods such as concatenative and parametric speech synthesis, speech synthesis based on hidden Markov models, and deep learning approaches like end-to-end models for building the acoustic model. The article discusses metrics for evaluating the quality of synthesized voice. Brief overviews of modern text-to-speech architectures, such as WaveNet, Tacotron, and Deep Voice, applying deep learning and demonstrating quality ratings close to professionally recorded speech, are also provided.

More by Orken Mamyrbayev

• A Knowledge-Augmented Multi-Agent AI System for Context-Aware Legal Reasoning and Case Outcome PredictionPreprints.org · 2026
• YOLO12 МОДЕЛІН ГЕОРАДАР КЕСКІНДЕРІНЕ ҚОЛДАНУNEWS OF THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHSTAN · 2026
• Developing a Kazakh Audio–Visual Multimodal Speech Recognition Model Based on Hierarchical and Cross-Modal AttentionInformation · 2026
• Exploring the influence of firm digital transformation on sustainable competitive advantage through organizational agility with moderation of knowledge sharingDiscover Sustainability · 2026