Performance Optimization and Practical Exploration of Transformer Architecture in Speech Synthesis

The Transformer architecture has been widely used in the field of speech synthesis due to its powerful modeling capabilities and flexibility. However, the existing Transformer architecture still encounters many performance limitations in practical operation, such as insufficient naturalness of sound quality, slow inference speed, poor cross language adaptability, and high computational resource consumption. In response to these issues, this article proposes a series of targeted performance optimization strategies, including improving the model architecture to enhance the naturalness of sound quality, accelerating the inference process to meet real-time requirements, enhancing the model's adaptability to multiple languages and cross domains, and adopting efficient algorithms and hardware optimization to reduce computational resource consumption. Through experimental verification, these optimization strategies effectively promote the performance improvement of speech synthesis systems, greatly enhancing their feasibility and efficiency in practical applications.