How it works

The Text-to-Speech (TTS) process involves several key phases that convert written text into natural-sounding speech using advanced neural network models prevalent in current technologies.

Text Analysis and Tokenization

The initial phase involves preparing the input text for processing. Modern TTS systems tokenize the text, breaking it down into smaller units called tokens, which can be words, subwords, or characters. This tokenization enables the model to handle varying lengths of text and capture linguistic nuances. The text is also normalized by correcting spelling errors, expanding abbreviations, and converting numbers and symbols into words to ensure accurate representation.

Text Encoding and Embedding

Once tokenized, the text tokens are converted into numerical representations that neural networks can process. This is achieved through embedding layers, where each token is mapped to a high-dimensional vector capturing semantic and syntactic information. These embeddings allow the model to understand context and relationships between tokens.

Acoustic Model Generation with Neural Networks

In this phase, neural network models, such as sequence-to-sequence models with attention mechanisms or Transformer architectures, predict acoustic features from the text embeddings. These models learn the mapping from text sequences to sequences of acoustic features, capturing elements like intonation, rhythm, and stress patterns to produce speech that sounds natural. The use of neural networks allows for end-to-end training, improving the model's ability to generalize and handle diverse linguistic inputs.

Voice Synthesis with Neural Vocoders

The predicted acoustic features are then converted into waveforms using neural vocoders. Modern vocoders like WaveNet, WaveGlow, and HiFi-GAN are based on deep learning and generate high-fidelity audio by modeling the complex patterns of human speech. These neural vocoders synthesize the voice by transforming the acoustic parameters into audible sound waves, resulting in realistic and expressive speech output.

Audio Output and Post-processing

Finally, the synthesized audio is processed to ensure clarity and consistency. Post-processing steps may include filtering to remove artifacts, normalizing the volume, and adjusting the pitch or speed if necessary. The final audio is then formatted according to the requirements of the application or device, ready for playback.