通过集成BGRU与Transformer架构提升语音质量
Enhancing Speech Quality through the Integration of BGRU and Transformer Architectures
AI总结：
本研究提出集成BGRU与Transformer的混合架构用于语音增强，通过捕捉时间依赖和复杂信号模式，显著提升噪声抑制与语音质量，优于传统及单一模型方法。
摘要：
语音增强在改善嘈杂环境中语音信号质量方面发挥着重要作用。本文研究了将双向门控循环单元（BGRU）与Transformer模型集成用于语音增强任务的有效性。通过全面的实验评估，我们的研究证明了这种混合架构相对于传统方法和单一模型的优越性。BGRU-Transformer组合框架擅长捕捉时间依赖关系和学习复杂信号模式，从而增强了噪声抑制能力并改善了语音质量。结果表明，与现有方法相比，性能显著提升，凸显了该集成模型在实际应用中的潜力。BGRU与Transformer架构的无缝集成不仅增强了系统鲁棒性，还为先进的语音处理技术开辟了道路。本研究为语音增强技术的持续努力做出了贡献，并为未来优化模型架构、探索多种应用场景以及推进嘈杂环境中语音处理领域的研究奠定了坚实基础。
英文摘要：
Speech enhancement plays an essential role in improving the quality of speech signals in noisy environments. This paper investigates the efficacy of integrating Bidirectional Gated Recurrent Units (BGRU) and Transformer models for speech enhancement tasks. Through a comprehensive experimental evaluation, our study demonstrates the superiority of this hybrid architecture over traditional methods and standalone models. The combined BGRU-Transformer framework excels in capturing temporal dependencies and learning complex signal patterns, leading to enhanced noise reduction and improved speech quality. Results show significant performance gains compared to existing approaches, highlighting the potential of this integrated model in real-world applications. The seamless integration of BGRU and Transformer architectures not only enhances system robustness but also opens the road for advanced speech processing techniques. This research contributes to the ongoing efforts in speech enhancement technology and sets a solid foundation for future investigations into optimizing model architectures, exploring many application scenarios, and advancing the field of speech processing in noisy environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
