AI voice text-to-speech (TTS) technology employes advanced algorithms and machine learning models to transform the written words into spoken form. One of many applications using this technology are virtual assistants, audiobooks and accessibility tools. The process has several main stages such as text-analysis, linguistic processing and the last — speech synthesis that is realized using deep-learning models.
TEXT ANALYSIS :- The first step is text analysis which take the input as a txt file and parses that to understand its structure content. It recognizes the language, grammar and punctuation in order to identify how a sentence should be pronounced or its rhythm on-the-fly. The AI model, as per a report from MIT Technology Review can handle more than 20 languages and even thousands of distinct speech patterns which help in maintaining highly accurate voices that sound very natural.
Finally, the linguistic processing is performed over that few text. In this step, it works down the text into phonemes which are smallest components of sound in a language. Finally, the AI applies a prosody to their text — which is what allows them to control pitch, tempo and volume of voices. In English, for instance, a rising pitch at the end of an sentence is commonly associated with questions. Top-of-the-line TTS systems, like Google's WaveNet models, require more than 1K+ hours of recorded speech data to meticulously tune prosody and hence deliver a most natural human-like voice. Use of neural network has lead to a great improvement in this regard, paving the way for human-like intonation and rhythm so that text-to-speech sounds less robotic than it used to do.
After that comes speech synthesis. This is where the AI creates the audio output. Deep Learning Models : Most TTS systems these days are based on various end-to-end deep learning models, especially GANs and Transformer-based that generate high quality speech. In practice, what happens is that GANs are actually two neural networks working together—the generator and the discriminator to iteratively improve speech output. A later study published in IEEE Transactions on Audio, Speech and Language Processing found that these models are 4–10% more accurate than classicial methods. Those are ranging from the same sort of monotone on a news broadcast all the way up to really emotional and storytelling type sounds.
In fact, Gartner says AI-driven voice assistants already handle 25% of all customer service interactions in some industries using AI voice text-to-speech technology. Automated systems from Salesforce save these businesses up to 70 cents-per-dollar in costs compared to human agents and makes their experience way more efficient.
Another important advantage of WordPress is its ability to be customized. In addition to which voice model that shall be used, it also selects the speech speed and what desired emotional tone (happy/neutral/sad) you would want. In some cases, the entertainment industry commonly uses AI to generate these voices for developing characters in video games or animated films that require meticulous vocal control. Azure TTS at Microsoft offers 75+ voices in multiple languages and dialects as necessary for global applications.
However, there are still problems that AI voice text-to-speech encounters. It is still very difficult to achieve perfect naturalness, particularly in the case of complex sentences and diverse accents. However, the distance between human and machine learning-generated speech is being bridged by leaps and bounds with these better models.
Learn more about ai voice text to speech by visiting DupDub, a platform that provides advanced TTS solutions for various use cases. It is a clear illustration of how the technology has evolved further to provide more human like voice with flexibility.