why are donut shops owned by asians - Finally, *synthesis techniques* play a vital role in converting the model's output into audible speech. *Neural vocoders* are commonly used to generate high-fidelity audio waveforms from the model's latent representations. These vocoders are trained to reconstruct audio from low-dimensional features, such as mel-spectrograms or linear predictive coding (LPC) coefficients. WaveNet, developed by DeepMind, is a popular neural vocoder that generates audio waveforms autoregressively, one sample at a time. This approach allows for highly realistic and natural-sounding speech, but it can be computationally expensive. Other vocoders, such as MelGAN and Parallel WaveGAN, offer faster synthesis speeds while maintaining good audio quality. These models use generative adversarial networks (GANs) to learn the mapping from mel-spectrograms to audio waveforms. The choice of vocoder depends on the trade-off between audio quality and computational efficiency. In addition to neural vocoders, traditional signal processing techniques, such as formant synthesis and articulatory synthesis, are still used in some applications. These techniques offer more control over the acoustic parameters of the speech, but they typically require more manual engineering.
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