Editing State-of-the-art (section)

===== Vainer, J., & Dušek, O. (2020). Speedyspeech: Efficient neural speech synthesis. ''arXiv preprint arXiv:2008.03802''. =====
* Summary: This paper introduces a novel student-teacher network architecture called "SpeedySpeech" for fast and high-quality neural speech synthesis. The system is designed to enable faster-than-real-time speech synthesis while requiring minimal computing resources, and deliver audio quality that is superior to existing models such as the Tacotron 2. The model uses the teacher network for duration extraction, the student network for spectrogram synthesis, and combines it with the MelGAN vocoder to output high-quality audio. The training process is efficient and can be completed in less than 40 hours on a single 8GB GPU. 
* RQ: How can we develop a neural speech synthesis system that does not require extensive computing resources while maintaining fast training times, fast inference, and high-quality audio output?
* Hypothesis: Assuming a student-teacher network architecture with simplified convolutional blocks and only a single attention layer in the teacher model, it is possible to surpass existing models in terms of training efficiency and audio quality while maintaining fast inference.
* Conclusion: The proposed SpeedySpeech model successfully achieves its goals by demonstrating that self-attention layers are not necessary for high-quality audio generation and that simpler, fully convolutional methods enable a more efficient training process and faster synthesis. The model's speech quality score is significantly higher than Tacotron 2, and it can be trained efficiently on a single GPU and even run in real time on the CPU.
* Critical observations: The article proposes ways to address the trade-off between training efficiency and audio quality in neural speech synthesis. By using only a single attention layer in the teacher model and eliminating sequence generation in the student network, the authors achieve important simplifications that increase model efficiency. In the model evaluation, the authors comprehensively considered objective indicators (such as MAE, SSIM) and subjective listening tests to provide a comprehensive assessment of model performance.
* Relevance: This speech synthesis model has applications in many fields, including virtual assistants, machine translation, etc. The SpeedySpeech model can synthesize speech in real time on moderate hardware, making it particularly suitable for deployment in resource-constrained environments. Additionally, the focus on efficiency and quality sets new benchmarks for future research and development in this area.