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AI speech synthesis is a form of technology that enables text to be converted into speech sounds that can imitate the human voice. It is also known as text-to-speech (TTS) or voice cloning. AI speech synthesis uses Deep Learning to create higher-quality synthetic speech that more accurately mimics the pitch, tone, and pace of a real human voice. AI speech synthesis can be used for various purposes, such as storytelling, news articles, audiobooks, voice assistants, and more. AI speech synthesis can also create custom voices or clone existing voices from samples or scratch.

Deep Learning is a branch of Machine Learning that uses deep neural networks (DNNs) to learn from large amounts of data and perform complex tasks. Deep Learning speech synthesis uses DNNs to produce artificial speech from text (text-to-speech) or spectrum (vocoder). The DNNs are trained using a large amount of recorded speech and, in some cases, the associated labels and/or input text. The DNNs learn to map the input (text or spectrum) to the output (spectrum or speech) by optimizing a loss function that measures the difference between the predicted and the target outputs. Depending on the architecture and the objective of the DNNs, they can perform different functions in speech synthesis, such as text analysis, acoustic modeling, voice cloning, voice tuning, etc.

Text-to-Speech (TTS)

Text-to-speech (TTS) is a technology that uses Artificial Intelligence to convert text into natural-sounding speech. TTS can be used for various purposes, such as creating voice messages, audio books, courses, and accessibility features for visually impaired users. TTS software can analyze and synthesize human speech patterns and linguistics using Natural Language Processing (NLP) and Deep Learning techniques. Some examples of TTS solutions are Google Cloud Text-to-Speech, Microsoft Azure Text to Speech, NaturalReader, VEED.IO, and Murf.