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ReadSpeaker text-to-speech voices are humanlike, relatable voices. There are 70+ voices available in 20+ languages, with more on their way. Meet the ReadSpeaker TTS family of high-quality voice personas and put them to the test.
To hear how our TTS voices read a text of your choice, scroll down to the bottom of the page.
This demo tool lets you enter your own text and sample some of the languages and voices that we offer.
Please note: not all languages and voices are available for every product.
At ReadSpeaker, we have a passion for developing high-quality TTS voices. In fact, expert third party industry observers rate the US English ReadSpeaker TTS voice as being the most accurate on the market. The enthusiastic feedback we receive from our customers confirms that we deliver the very best TTS solutions for successful online, offline, embedded and server-based applications around the world. Our commitment to providing outstanding TTS solutions is made possible by our uncompromising production process, designed to guarantee the quality levels that have earned ReadSpeaker TTS the trust of customers from across countries and markets.
To create our speech personas, we select and record professional voice talents. In the resulting speech database, each utterance is segmented into individual parts, such as phones, syllables, and words. We then apply a technique called Unit Selection Synthesis (USS). USS selects segments (units) of speech that can be ‘glued’ together in such a way that high-quality synthetic speech is produced.
Once a voice talent has been selected, she or he works with our voice development team for several weeks. A diverse script is used for the recordings, designed to contain all the sound patterns of the language in development. The team closely monitors the recording process to check for consistency in pronunciation, accentuation, and style.
In the second phase of TTS voice creation, a rich mark-up is added to the speech recordings. Each word, phoneme and stress is annotated as well as several other aspects. The technical team works its magic on this process – using a powerful combination of Artificial Intelligence and machine learning technologies on big amounts of data to optimize annotations. Our state-of-the-art methodologies are augmented by the linguistic expertise of our team. The resulting database is used by the ReadSpeaker TTS engine to convert text into speech spoken by the TTS voice.
This is how a new ReadSpeaker TTS voice persona is born. However, the process doesn’t end there. One of ReadSpeaker’s unique characteristics is our ongoing improvement process. Through a system of high-quality feedback and a thorough Quality Assurance process by mother-tongue experts, imperfections are continuously corrected.
In parallel, ReadSpeaker is also working on the future of text to speech by developing techniques based on deep learning. Instead of USS, this revolutionary technique involves mapping linguistic properties to acoustic features using Deep Neural Networks (DNNs). This technique uses an iterative learning process to minimize objectively measurable differences between the predicted acoustic features and the observed acoustic features in the training set. One of the advantages of the new DNN TTS method is that the acoustic database can be much smaller than for a USS voice. This makes developing new, smart ReadSpeaker TTS voices with even more lifelike, expressive speech and customizable intonation faster than ever.
If your strategy is to offer an exclusive customer experience and you want to take your brand appeal to a new level, one of the most powerful ways to differentiate yourself is by using a custom voice to represent you. A custom voice sets your brand apart and creates a powerful bond with your customers across your various communication touchpoints. If a preferred celebrity or other talent reflects your brand best and you want to be able to use their voice anytime you need it, ReadSpeaker can create a custom TTS voice powered by our leading-edge speech engine, to give your brand instant recognition in the voice user interface.