Generative adversarial networks (GANs) apply to text-to-speech (TTS) by improving the quality and naturalness of synthesized speech through adversarial training. In a typical GAN setup for TTS, a generator model creates speech waveforms or intermediate representations (like mel-spectrograms) from text input, while a discriminator model evaluates whether the output resembles real human speech. The generator aims to fool the discriminator, and the discriminator provides feedback to refine the generator’s output. This adversarial process helps reduce artifacts and over-smoothing common in traditional TTS systems that rely solely on mean squared error (MSE) loss.
One specific example is GAN-TTS, where the generator converts text embeddings into mel-spectrograms, and the discriminator uses both spectral and temporal features to assess realism. Unlike autoregressive models (e.g., WaveNet), which generate speech sequentially and are slow, GANs can produce high-quality speech in parallel, making them faster for real-time applications. Another example is Parallel WaveGAN, a vocoder that uses GANs to synthesize raw audio from mel-spectrograms. Here, the generator predicts waveform samples directly, and the discriminator evaluates local and global consistency of the generated audio. This approach reduces the computational cost of traditional vocoders while maintaining clarity.
However, GAN-based TTS systems face challenges. Training instability is common, requiring careful tuning of loss functions (e.g., combining adversarial loss with MSE or spectral convergence loss). Mode collapse—where the generator produces limited speech variations—can occur if the discriminator becomes too dominant. Additionally, integrating GANs into full TTS pipelines (e.g., aligning text features with audio outputs) remains complex. Despite these issues, GANs are increasingly used in hybrid systems, such as combining a GAN-based mel-spectrogram generator with a neural vocoder, to balance speed and quality. Developers working on TTS can leverage open-source implementations like NVIDIA’s WaveGlow or HiFi-GAN to experiment with GAN-driven improvements in speech synthesis.