标题： 说话人验证的说话人文本因子分解嵌入文本适应 显示英文标题

标题： Text adaptation for speaker verification with speaker-text factorized embeddings

摘要： 预收集数据（无论是训练数据还是注册数据）与实际测试数据之间的文本不匹配会显著影响文本依赖的说话人验证（SV）系统性能。 尽管可以通过仔细收集具有目标语音内容的数据来解决这个问题，但这种数据收集可能成本高昂且不够灵活。 在本文中，我们提出了一种新颖的文本适应框架来解决文本不匹配问题。 在这里，提出了一种说话人-文本因子分解网络，将输入语音分解为说话人嵌入和文本嵌入，然后在后续阶段将它们集成到一个表示中。 给定少量与说话人无关的适应语音，可以提取目标语音内容的文本嵌入，并用于将与文本无关的说话人嵌入适应为定制文本的说话人嵌入。 在RSR2015上的实验表明，文本适应可以显著提高文本不匹配条件下的性能。 显示英文摘要

摘要： Text mismatch between pre-collected data, either training data or enrollment data, and the actual test data can significantly hurt text-dependent speaker verification (SV) system performance. Although this problem can be solved by carefully collecting data with the target speech content, such data collection could be costly and inflexible. In this paper, we propose a novel text adaptation framework to address the text mismatch issue. Here, a speaker-text factorization network is proposed to factorize the input speech into speaker embeddings and text embeddings and then integrate them into a single representation in the later stage. Given a small amount of speaker-independent adaptation utterances, text embeddings of target speech content can be extracted and used to adapt the text-independent speaker embeddings to text-customized speaker embeddings. Experiments on RSR2015 show that text adaptation can significantly improve the performance of text mismatch conditions.