How Machine Learning and Text to Speech Work Together

Machine learning and Artificial intelligence are being implemented in all sorts of fields. They not only help companies automate their services, but they continually grow and improve as more data is used. Text to speech services have benefited from machine learning greatly because so much data has been absorbed. They’ve been able to properly vocalize text in the correct ways based on data of that same text being used before, leading to words being pronounced more accurately, the context of the rest of
the sentence being taken into account, and faster results overall.

How Machine Learnings Adapts Text to Speech

Machine learning works by gathering data and retaining it for future use. As more data, in this case, the text is absorbed and converted to human speech, it can be done with a greater degree of accuracy because that data is already in place. For example, the first time someone converts the phrase “Have a nice day”, the text to speech service will have to analyze each letter and word individually to create the spoken phrase. The next time that phrase is used, however, it can be instantaneous because the software has learned from its past experience.

With these machine learning systems in place, everyday users get benefits like the more natural text to speech voices that sound less like a computer reading strung up letters, but more like a living person that is actually communicating.

A text to voice library or database gathers these words and phrases all together in a pool to determine for future use. Every time text is converted to speech, the software aims to learn from the experience and provide better results. Algorithms and artificial intelligence are used to sort through all that data gathered and determine not only how to use it, but how it compares to results before and whether or not those results were
successful.

How Text to Speech Services Will Be Improved

Everything contributes to this database, from the new words that it has never seen before, or the different uses of the same word. For example, the word “bow” can either be a medieval weapon, a tool used to play the violin, a special kind of knot, or a part of a ship. A machine learning software aims to find out which to use, and each one is used correctly for each instance.

These databases are so big that they contain limitless words and phrases, most of the time with different meanings and contexts attached. This is a big part of the reason why text to speech can get faster and more accurate without any hands-on maintenance.

As these databases grow more, the question will become less about how to program accurate text to speech services, but more about how to gather enough data that the program can learn to do it entirely by itself, essentially making the entire process entirely autonomous.

It’s likely that in the future, we will see much more natural text to voice generated instantly, and this service will find even more useful in people's daily lives as machine learning helps it grow all the more.

 

 

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