Text-to-speech (TTS) systems convert written text into spoken language through a multi-step process that involves analyzing the input text, generating linguistic features, and synthesizing audible speech. The process starts with preprocessing the text to handle abbreviations, numbers, and special characters. For example, "Dr." becomes "Doctor," and "100" is converted to "one hundred." The system then breaks the text into smaller units like sentences or phrases and applies linguistic rules to determine pronunciation, stress, and intonation. This step, called text normalization, ensures the system interprets the text correctly before generating speech.
Next, the system uses synthesis methods to convert processed text into sound. Traditional approaches include concatenative synthesis, which stitches together pre-recorded speech fragments (like syllables or words) from a database. For instance, saying "cat" might combine sounds for "k," "a," and "t." Another method, parametric synthesis, generates speech from scratch using mathematical models that simulate vocal tract movements and sound waves. These models define parameters like pitch, duration, and frequency to produce natural-sounding speech. While concatenative methods sound more natural, they require large datasets and lack flexibility. Parametric methods are more adaptable but often sound robotic.
Modern TTS systems use deep learning to improve quality. Neural networks, like WaveNet or Tacotron, analyze vast amounts of speech data to learn patterns in pronunciation, rhythm, and tone. For example, WaveNet generates raw audio waveforms by predicting each sound sample based on previous ones, creating highly natural speech. These models also handle context, such as adjusting intonation for questions versus statements. By training on diverse datasets, they produce speech that adapts to different accents or speaking styles. This approach combines the flexibility of parametric synthesis with the naturalness of concatenative methods, resulting in more human-like output.