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

==== Multi-Modal Speech Synthesis ====
Multi-modal speech synthesis refers to the generation of synthetic speech that is not only audible but also visually coherent with facial movements, as we mentioned before in Key Innovations: articulatory features-based TTS. Neural network models, especially generative models like Generative Adversarial Networks (GANs), have been pivotal in synthesizing realistic visual representations (like lip movements) corresponding to synthesized or real speech.<ref>[https://arxiv.org/pdf/1807.07860.pdf Hang Zhou, Yu Liu, Ziwei Liu, Ping Luo, Xiaogang Wang. Talking Face Generation by Adversarially Disentangled Audio-Visual Representation]</ref>

Advantages:

* Enhanced User Experience: Multi-modal synthesis provides a richer and more immersive user experience by aligning visual cues with synthesized speech.
* Accessibility: It can enhance communication accessibility, especially for individuals with hearing impairments, by providing visual speech cues.
* Realistic Virtual Interactions: It enables the creation of realistic virtual characters or digital humans for applications in virtual reality, gaming, and online communication.

Challenges:

* Lip Synchronization: Ensuring that the synthesized speech is perfectly synchronized with the lip movements to avoid uncanny valley experiences.
* Expressiveness: Maintaining natural facial expressions and emotions while ensuring lip synchronization can be complex.
* Data Requirements: Acquiring high-quality, synchronized audio-visual data for training models can be challenging and resource-intensive.
* Computational Complexity: Managing and processing multiple modalities (audio and visual) requires significant computational resources and optimized algorithms.