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

== Future research ==

==== 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.

==== Efficient speech synthesis ====
Achieving high-quality speech synthesis propels us towards the pivotal task of efficient synthesis, which encompasses minimizing the costs associated with speech synthesis, such as data collection, labeling, and TTS model training and serving.

Modern neural TTS systems, while capable of synthesizing exquisite speech, typically utilize substantial neural networks, often inhibiting applications in resource-constrained devices like mobiles and IoT due to their extensive memory and power demands. Thus, crafting models that are both compact and lightweight, ensuring reduced memory usage, power consumption, and latency, becomes imperative for such applications.

Moreover, the energy-intensive and carbon-emitting nature of training and serving top-tier TTS models necessitates enhancements in energy efficiency, such as diminishing the FLOPs in TTS training and inference, to broaden accessibility to advanced TTS technologies while concurrently mitigating environmental impact.

Challenges:

* Balancing Quality and Efficiency: Crafting models that are lightweight yet do not compromise on the quality of speech synthesis.
* Adaptability: Ensuring that efficient models can adapt to various speakers, emotions, and styles with limited resources.
* Energy-Efficient Training: Developing training methodologies that require less computational power without sacrificing the learning capability of the models.
* Low-Resource Adaptation: Ensuring the models can perform optimally even in environments with restricted computational and memory resources.
* Environmental Sustainability: Aligning the development and usage of TTS technologies with environmental sustainability goals, ensuring that advancements do not exacerbate carbon emissions.

==== Cross-Lingual and Multi-Lingual Speech Synthesis ====
Cross-lingual and multi-lingual speech synthesis in the realm of Neural Network-based Text-to-Speech (TTS) systems is an intriguing and complex domain, aiming to generate synthesized speech across various languages seamlessly. This area is particularly vital for creating TTS systems that can cater to a global audience, ensuring that technology is accessible and usable across linguistic boundaries.

Firstly, envisioning a future where a single TTS model seamlessly generates speech across multiple languages, the development of a unified phonetic representation becomes imperative. This representation would not only encapsulate the phonetic intricacies of various languages but also serve as a linchpin, enabling the TTS system to navigate through the phonetic landscapes of different languages with finesse.<ref>[https://aclanthology.org/P19-3011/ Sungjin Lee, Qi Zhu, Ryuichi Takanobu, Zheng Zhang, Yaoqin Zhang, Xiang Li, Jinchao Li, Baolin Peng, Xiujun Li, Minlie Huang, Jianfeng Gao. ConvLab: Multi-Domain End-to-End Dialog System Platform]</ref>

Moreover, the exploration and advancement of transfer learning techniques hold the potential to bridge the gap between data-rich and data-scarce languages. By harnessing knowledge from languages with abundant data, the technology can be finessed to enhance speech synthesis in languages that are traditionally data-limited, thereby broadening the linguistic horizons of the TTS system.<ref>[https://arxiv.org/abs/1806.04558 Ye Jia, Yu Zhang, Ron J. Weiss, Quan Wang, Jonathan Shen, Fei Ren, Zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio Lopez Moreno. Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis]</ref>

Simultaneously, the future beckons a deeper dive into adaptive prosody modeling, where the system would dynamically modulate the prosodic elements of synthesized speech to align with the specific contours of the target language. This ensures that the speech is not only linguistically accurate but also rhythmically and melodically congruent with the natural prosody of the language.

Furthermore, embedding cultural and emotional nuances in synthesized speech emerges as a pivotal frontier. The future TTS system would not merely be a linguistic translator but a cultural and emotional interpreter, ensuring that the synthesized speech resonates authentically, both linguistically and emotionally, across varied cultural contexts.

In synthesizing these pathways—crafting a unified phonetic representation, leveraging transfer learning, delving into adaptive prosody modeling, and embedding cultural and emotional nuances—the future of TTS technology is sculpted. A future where the technology is not just a tool for linguistic translation but a conduit for authentic, emotionally resonant, and culturally rich communication across a tapestry of languages and cultures.

Challenges:

* Phonetic and Prosodic Variations: Different languages have distinct phonetic and prosodic characteristics. Modeling these variations accurately to generate natural-sounding speech in multiple languages is challenging.
* Data Scarcity: For some languages, especially minority or less-resourced ones, there is a scarcity of quality data to train robust models, which hinders the development of universal multi-lingual TTS systems.
* Accent and Dialect Preservation: Preserving native accents and dialects while ensuring clarity and naturalness in synthesized speech across different languages is a complex task.