用于文本语音对齐的自适应时长模型
Adaptive Duration Model for Text Speech Alignment
AI总结：
针对TTS语音文本对齐问题，提出一种自适应音素级时长预测框架，可提升对齐精度并增强零样本TTS对音频不匹配的鲁棒性。
摘要：
语音到文本对齐是神经文本转语音（TTS）模型的关键组成部分。自回归TTS模型通常使用注意力机制在线学习这些对齐，而非自回归端到端TTS模型则依赖从外部来源提取的时长。本文提出了一种新颖的时长预测框架，能够在给定文本的情况下给出有前景的音素级时长分布。实验中，与以往的基线模型相比，所提出的时长模型具有更精确的预测能力和对条件的适应能力。具体而言，它在音素级对齐准确率上取得了显著提升，并使零样本TTS模型在提示音频与输入音频不匹配时的性能更加稳健。
英文摘要：
Speech-to-text alignment is a critical component of neural text to speech (TTS) models. Autoregressive TTS models typically use an attention mechanism to learn these alignments on-line, while non-autoregressive end to end TTS models rely on durations extracted from external sources. In this paper, we propose a novel duration prediction framework that can give promising phoneme-level duration distribution with given text. In our experiments, the proposed duration model has more precise prediction and adaptation ability to conditions, compared to previous baseline models. Specifically, it makes a considerable improvement on phoneme-level alignment accuracy and makes the performance of zero-shot TTS models more robust to the mismatch between prompt audio and input audio.
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。
