Triple M：一个实用的多引导注意多带多时LPCNet的文本到语音合成系统
Triple M: A Practical Text-to-speech Synthesis System With Multi-guidance Attention And Multi-band Multi-time LPCNet
摘要：
在这项工作中，一个强大的和高效的文本到语音（TTS）合成系统命名为三重M提出了大规模的在线应用。三重M的关键组成部分是：1）序列到序列模型采用了一种新的多指导注意力转移互补的优势，从指导注意力机制的基本注意力机制没有域内的性能损失和在线服务修改。与单一注意力机制相比，多引导注意不仅给长句合成带来了更好的自然度，而且单词出错率降低了26.8%。2)提出了一种新的高效多带多时声码器框架，将计算复杂性从2.8 GFLOP降低到1.0 GFLOP，并在单个CPU上将LPCNet的速度提高了2.75倍。
英文摘要：
In this work, a robust and efficient text-to-speech (TTS) synthesis system named Triple M is proposed for large-scale online application. The key components of Triple M are: 1) A sequence-to-sequence model adopts a novel multi-guidance attention to transfer complementary advantages from guiding attention mechanisms to the basic attention mechanism without in-domain performance loss and online service modification. Compared with single attention mechanism, multi-guidance attention not only brings better naturalness to long sentence synthesis, but also reduces the word error rate by 26.8%. 2) A new efficient multi-band multi-time vocoder framework, which reduces the computational complexity from 2.8 to 1.0 GFLOP and speeds up LPCNet by 2.75x on a single CPU.
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。
