RephraseTTS：基于动态长度文本的说话人风格迁移语音插入
RephraseTTS: Dynamic Length Text based Speech Insertion with Speaker Style Transfer
AI总结：
针对文本条件语音插入任务，提出基于Transformer的非自回归方法RephraseTTS，可动态确定可变长度插入语音并保持说话人风格，在LibriTTS上优于基线方法。
摘要：
我们提出了一种文本条件语音插入任务的方法，即在输入语音样本中插入一段语音样本，并以对应的完整文本转录为条件。该任务的一个示例用例是在对相应文本转录进行校正时更新语音音频。所提出的方法采用基于Transformer的非自回归方法，允许根据文本转录和可用部分输入的节奏，在推理过程中动态确定可变长度的语音插入。它能够保持说话人的语音特征、韵律以及可用语音输入的其他频谱特性。我们在LibriTTS上的实验和用户研究结果表明，我们的方法优于基于现有自适应文本转语音方法的基线。我们还提供了大量定性结果，以展示所提出方法的输出质量。
英文摘要：
We propose a method for the task of text-conditioned speech insertion, i.e. inserting a speech sample in an input speech sample, conditioned on the corresponding complete text transcript. An example use case of the task would be to update the speech audio when corrections are done on the corresponding text transcript. The proposed method follows a transformer-based non-autoregressive approach that allows speech insertions of variable lengths, which are dynamically determined during inference, based on the text transcript and tempo of the available partial input. It is capable of maintaining the speaker's voice characteristics, prosody and other spectral properties of the available speech input. Results from our experiments and user study on LibriTTS show that our method outperforms baselines based on an existing adaptive text to speech method. We also provide numerous qualitative results to appreciate the quality of the output from the proposed method.
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。
