The article explores modern methods of AI-powered speech synthesis in 2026. The author notes that despite the high sophistication of models capable of mimicking intonations and emotions, the main challenge remains the quality of source text preparation. Neural networks often misinterpret specific terms, abbreviations, dates, or financial figures, which diminishes listener trust. The material provides practical recommendations on text preprocessing, selecting appropriate models for specific tasks, and the legal aspects of using synthesized audio in commercial projects. It also includes a comparative analysis of service costs, limits, and technical constraints of popular platforms like BotHub. The article emphasizes that achieving professional results requires not only the power of the algorithm but also meticulous content preparation before generation.
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