What are the core components of a TTS system?

A text-to-speech (TTS) system converts written text into spoken audio and consists of three core components: text processing, acoustic modeling, and waveform synthesis. Each component handles specific stages of the transformation, ensuring the output is intelligible and natural-sounding. Let’s break these down in detail.

The first component, text processing, prepares raw text for synthesis. This involves normalizing abbreviations, numbers, and symbols (e.g., converting “$20” to “twenty dollars” or “Dr.” to “Doctor”), segmenting sentences into words or subword units, and analyzing linguistic features like part-of-speech tags. For example, the word “read” might be pronounced differently depending on context (“I will read” vs. “I read yesterday”). Phonetic conversion is also critical here, where words are mapped to their phonetic representations using rules or dictionaries (e.g., converting “cat” to /kæt/). Prosody prediction—determining rhythm, stress, and intonation—is often part of this stage, as it influences how natural the speech sounds.

The second component, acoustic modeling, generates the acoustic features that represent speech. Modern TTS systems typically use neural networks (e.g., Tacotron, FastSpeech) trained on paired text-audio data to predict features like mel-spectrograms. These features capture the timbre, pitch, and timing of speech. For instance, a model might learn that a question mark at the end of a sentence requires a rising pitch. The quality of the acoustic model directly impacts the naturalness of the output. Some systems use duration models to align phonemes with specific time spans, ensuring syllables aren’t cut off or stretched unnaturally.

The final component, waveform synthesis (or vocoding), converts acoustic features into audible speech. Traditional vocoders like Griffin-Lim reconstruct waveforms from spectrograms but often produce robotic sounds. Neural vocoders like WaveNet or HiFi-GAN use deep learning to generate high-fidelity audio. For example, WaveNet processes mel-spectrograms to produce raw waveform samples at 24 kHz, capturing subtle details like breath sounds. The vocoder’s efficiency affects real-time performance, while its accuracy determines clarity. Together, these components form a pipeline where text is first analyzed, then mapped to sound characteristics, and finally rendered as audible speech.