Text-to-Speech creates raw audio data of natural, human speech. That is, it creates audio that sounds like a person talking. When you send a synthesis request to Text-to-Speech, you must specify a voice that 'speaks' the words.
There are a wide selection of custom voices available for you to pick from in Text-to-Speech. The voices differ by language, gender, and accent (for some languages). Some languages have multiple voices to choose from. You can see a list of the voices available for speech synthesis in the Text-to-Speech on the Supported Voices page.
The voices offered from Text-to-Speech can also differ in how they are produced, the synthetic speech technology used to create the machine model of the voice. One common speech technology, parametric text-to-speech, typically generates audio data by passing outputs through signal processing algorithms known as vocoders. Many of the standard voices available in Text-to-Speech use a variation of this technology.
The Text-to-Speech API also offers a group of premium voices generated using a WaveNet model, the same technology used to produce speech for Google Assistant, Google Search, and Google Translate. WaveNet technology provides more than just a series of synthetic voices: it represents a new way of creating synthetic speech.
A WaveNet generates speech that sounds more natural than other text-to-speech systems. It synthesizes speech with more human-like emphasis and inflection on syllables, phonemes, and words. On average, a WaveNet produces speech audio that people prefer over other text-to-speech technologies.
Figure 1. Chart showing comparison of WaveNet to other synthetic voices, human speech. The y-axis values represent the Mean Opinion Score (MOS) for each voice. Test subjects ranked each voice on a scale of 1-5 according to how much it sounded like natural speech. For more information on MOS scores and WaveNet technology, see the DeepMind WaveNet page.
Unlike most other text-to-speech systems, a WaveNet model creates raw audio waveforms from scratch. The model uses a neural network that has been trained using a large volume of speech samples. During training, the network extracts the underlying structure of the speech, such as which tones follow each other and what a realistic speech waveform looks like. When given a text input, the trained WaveNet model can generate the corresponding speech waveforms from scratch, one sample at a time, with up to 24,000 samples per second and seamless transitions between the individual sounds.
To hear the difference between a Wavenet-generated audio clip and a clip generated by another text-to-speech process, compare the two audio clips below.
Example 1. High quality, non-WaveNet voice
Example 2. WaveNet voice
To learn more about WaveNet models, read this blog post by DeepMind.