Concatenative and parametric text-to-speech (TTS) systems differ in their underlying approaches to generating speech. Concatenative TTS uses a database of pre-recorded speech segments (like words, syllables, or phonemes) and combines them to form sentences. For example, a system might stitch together recordings of the word "hello" and "world" to say "hello world." This method relies on having a large, high-quality dataset of spoken units. If the required units are present and match the target context, the output sounds natural. However, gaps in the database or mismatches between units (like differing intonation) can lead to robotic or inconsistent speech.
Parametric TTS, on the other hand, generates speech from scratch using mathematical models. Instead of stitching audio clips, it produces speech parameters (like pitch, duration, and spectral features) using algorithms such as Hidden Markov Models (HMMs) or neural networks. These parameters are then converted to audio using a vocoder. For example, a parametric system like WaveNet or Tacotron uses deep learning to predict raw audio waveforms directly. This approach is more flexible, as it can synthesize new words or accents not explicitly in a dataset. However, early parametric systems often sounded less natural than concatenative ones due to limitations in modeling and vocoder quality.
The key trade-offs lie in flexibility, naturalness, and computational demands. Concatenative systems excel in naturalness when the database is comprehensive but struggle with dynamic changes (e.g., new vocabulary or speaking styles). Parametric systems adapt better to new contexts and allow finer control over speech characteristics (e.g., emotion or speed) but require more computational power and advanced modeling to achieve high quality. Modern hybrid approaches, like using neural networks to enhance concatenative methods, aim to balance these strengths. Developers choose between them based on factors like use case (static vs. dynamic content), resource availability, and desired speech quality.