A vocoder in text-to-speech (TTS) systems generates the final audio waveform from intermediate acoustic features. It acts as the last step in the synthesis pipeline, converting numerical representations like spectrograms or mel-frequency cepstral coefficients (MFCCs) into audible speech. Without a vocoder, TTS systems would lack the ability to produce actual sound, as earlier stages of processing only handle linguistic and prosodic features, not raw audio.
The technical process involves reconstructing a time-domain waveform from spectral and pitch information. For example, neural TTS models like Tacotron 2 first produce a mel-spectrogram, which encodes frequency content over time but lacks phase data. The vocoder’s job is to infer the phase details and synthesize a waveform that matches the target speech characteristics. This requires solving an inverse problem: determining which waveform aligns with the provided spectral features while sounding natural. Modern neural vocoders, such as WaveNet or HiFi-GAN, use deep learning to model the relationship between acoustic features and waveforms, enabling high-fidelity output with reduced artifacts compared to older rule-based methods like STRAIGHT.
The choice of vocoder significantly impacts speech quality. Traditional vocoders often produced robotic or buzzy sounds due to oversimplified assumptions about speech structure. Neural vocoders, trained on large datasets, capture nuances like breath sounds and vocal cord vibrations more accurately. For instance, WaveGlow leverages normalizing flows to generate waveforms efficiently, while Parallel WaveGAN focuses on real-time synthesis. Developers integrating TTS systems must balance computational cost, latency, and output quality when selecting a vocoder, as these factors directly affect user experience in applications like voice assistants or audiobook generation.