Editing Advancements in Neural Network-Based TTS (2000s) (section)

====== <big>[[wikipedia:Vocoder|Vocoder]]</big> ======
'''WaveNet:''' WaveNet serves as a vital component in Tacotron 2 for speech synthesis. It transforms mel-scale spectrograms, predicted by the feature prediction network, into time-domain waveform samples, resulting in high-quality audio waveforms.In this architecture, WaveNet is adapted to function as a vocoder. It takes the predicted mel spectrograms and uses dilated convolutional layers organized into dilation cycles.<ref name=":0" /> 

'''Innovation:'''

* Dilated Causal Convolutions: Utilizes dilated causal convolutions to exponentially expand the receptive field with the number of layers.
* Gated Activation Units: Incorporates gated activation units to regulate information flow within the network.
* Skip Connections: Employs skip connections for the network to learn residual functions.
* Softmax Output Layer: Utilizes a softmax output layer to model the probability distribution over the next audio sample.
* Hierarchical Structure: Adopts a hierarchical structure to model audio at multiple scales.<ref name=":0">Wang, Y., Skerry-Ryan, R. J., Stanton, D., Wu, Y., Weiss, R. J., Jaitly, N., ... & van den Oord, A. (2017). Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions. arXiv preprint arXiv:1712.05884.</ref>


'''Parallel WaveGAN:''' Parallel WaveGAN is a waveform generator utilizing GANs. It employs non-autoregressive WaveNet with multi-resolution spectrogram and adversarial loss functions for high-quality speech waveform synthesis. Unlike traditional models, it doesn't require complex density distillation, offering faster, efficient, small-footprint, and competitive waveform generation suitable for real-time applications.

'''Innovation:'''

* Non-Autoregressive WaveNet Generator: Utilizes a non-autoregressive WaveNet generator for faster and more efficient speech synthesis compared to traditional autoregressive models.
* Multi-Resolution Spectrogram and Adversarial Loss Functions: Trains the generator using multi-resolution spectrogram and adversarial loss functions to capture realistic speech waveforms' time-frequency distribution.
* No Density Distillation Required: Simplifies the training process by eliminating the need for density distillation, enhancing overall model efficiency.
* High-Fidelity Speech Generation with Small Model: Achieves high-fidelity speech synthesis with a compact model, suitable for real-time applications.
* Faster-Than-Real-Time Inference Speed: Provides inference speeds faster than real-time, making it ideal for real-time applications.
* Competitive Performance: Achieves competitive performance compared to other waveform generation models, ensuring high-quality speech synthesis.<ref>Yamamoto, R., Inoue, K., Portnoff, M., Tan, X., Inoue, S., Yamamoto, H., ... & Watanabe, S. (2020). Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram. arXiv preprint arXiv:1910.11480.</ref>