Text to speech with an AI voice generator

Overview

Realistic AI voice generators, also known as text-to-speech (TTS) systems, have come a long way in recent years. These systems use advanced machine learning algorithms to convert written text into spoken words, allowing computers to communicate with humans in a more natural and intuitive way. In this blog post, we will take a closer look at how realistic AI voice generators work, and explore some of the key differences between the major players in this field, including Amazon Polly, Google Cloud TTS, and Microsoft Azure’s TTS AI voice generators.

An important point to note is that, although the text-to-speech AI voice generators listed below are catered towards developers, most popular AI voice generators use them as their foundation. So, although the apps may differ, the technology at their core may be the same or very similar.

Amazon Polly

One of the most important components of an AI voice generator is the “engine” that powers it. Different companies use different types of engines to generate speech, and each has its own strengths and weaknesses. For example, Amazon Polly offers two different engines for generating speech:

Standard engine
Neural engine

The Standard engine is based on traditional TTS technology, and it is designed to produce speech that is easy to understand and has a relatively high level of accuracy. The Neural engine, on the other hand, is based on deep learning and neural networks, and it is designed to produce speech that is more natural and realistic.

Google Cloud TTS

Another major player in the field of realistic AI voice generators is Google Cloud TTS. Google offers three different engines:

Standard
Neural2
Wavenet

The Standard engine is similar to Amazon’s Standard engine, it’s designed to produce accurate and easy-to-understand speech. The Neural2 engine is a more advanced version of the Standard engine, which uses deep neural networks to produce more natural-sounding speech. The Wavenet engine is the most advanced engine offered by Google, it uses a neural network architecture known as the WaveNet to produce speech that is virtually indistinguishable from human speech.

Microsoft Azure TTS

Microsoft’s TTS system also uses a neural engine which is designed to produce natural-sounding speech. The neural engine is based on a deep learning algorithm called the Deep Neural Network TTS (DNN-TTS) which generates speech by predicting the acoustic features of speech from the input text. The DNN-TTS algorithm is trained on a large dataset of human speech and it can produce speech in multiple languages.

Personal review

After personally sampling and using multiple voices from each of these providers, the best in my opinion are as follow:

Microsoft Azure TTS
Google Cloud TTS
Amazon Polly

While all the listed providers have powerful engines and realistic-sounding voices, Microsoft provides the most flexibility and the most realistic sounding voices in my opinion. They offer more languages, more voices, and more customizations in voice generation thanks to their different speaking styles that mimic a range of emotions, like cheerful, sad, embarrassed, excited, and more.

Google Cloud TTS works well too, especially their WaveNet voices, but the selection at the time of this writing is not as wide, and there is no customization for speaking style or emotions like Microsoft Azure TTS, though they still have plenty of standard customizations, like pitch, rate, volume, and more.

Amazon Polly has been the simplest to use and integrate but that’s about as much as I can say for them.

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