Text-to-speech (TTS) systems convert written text into spoken language through a multi-step process that involves analyzing the input, generating linguistic features, and synthesizing audio. At a high level, TTS breaks down into three main stages: text processing, acoustic modeling, and waveform generation. Each stage uses specialized algorithms to transform the input into increasingly detailed representations, ultimately producing natural-sounding speech. Modern systems often rely on neural networks to handle these tasks, replacing older rule-based or concatenative approaches.
The first stage, text processing, involves parsing and normalizing the input text. This includes tasks like expanding abbreviations (e.g., “Dr.” to “Doctor”), converting numbers to words (“2024” to “twenty twenty-four”), and determining pronunciation rules. For example, the word “read” might be pronounced differently depending on context (“I will read” vs. “I read yesterday”). Systems use techniques like part-of-speech tagging and grapheme-to-phoneme conversion to resolve ambiguities. Some TTS engines also predict prosody—the rhythm, stress, and intonation of speech—by analyzing sentence structure and punctuation. For instance, a question mark might trigger a rising pitch at the end of a sentence. Libraries like Google’s Text Normalization or open-source tools like Festival Speech Engine handle many of these preprocessing steps.
The next stage, acoustic modeling, maps linguistic features to audio representations. Neural networks, such as Tacotron 2 or FastSpeech, are commonly used here. These models take phonemes (distinct sound units), duration, and pitch information as input and generate spectrograms—visual representations of sound frequencies over time. For example, the phoneme /k/ in “cat” would correspond to specific high-frequency bursts. The model learns these relationships from hours of recorded speech data. Finally, a vocoder (like WaveNet or Griffin-Lim) converts the spectrogram into raw audio waveforms. This step reconstructs the time-domain signal that speakers can play back. Modern TTS pipelines often combine these components into end-to-end systems, such as NVIDIA’s RAD-TTS, which streamline the process by jointly optimizing text and audio processing.
In practice, developers can implement TTS using APIs like AWS Polly, open-source frameworks like Mozilla TTS, or low-level libraries like PyTorch. For example, a navigation app might use AWS Polly’s SynthesizeSpeech API to turn directions like “Turn left in 200 meters” into speech. Under the hood, the API would normalize the text, predict phonemes and prosody, generate a spectrogram, and render it as an MP3 file. Customization options, such as adjusting speaking rate or voice style, are handled by modifying parameters in the acoustic model or vocoder. While modern TTS systems achieve near-human quality for many use cases, challenges remain in handling rare words, emotional tone, and low-resource languages, which continue to drive research in the field.