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

==== 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.