Target Speaker Extraction (TSE) aims to extract the clean speech of the target speaker in an audio mixture, eliminating irrelevant background noise and speech. While prior work has explored various auxiliary cues including pre-recorded speech, visual information, and spatial information, the acquisition and selection of such strong cues are infeasible in many practical scenarios. Differently, in this paper, we condition the TSE algorithm on semantic cues extracted from limited and unaligned text contents, such as condensed points from a presentation slide. This method is particularly useful in scenarios like meetings, poster sessions, or lecture presentations, where acquiring other cues in real time may be challenging. To this end, we design two different networks. Specifically, our proposed Text Prompt Extractor Network (TPE) fuses audio features with content-based semantic cues to facilitate time-frequency mask generation to filter out extraneous noise. The experimental results show the efficacy in accurately extracting the target speaker's speech by utilizing semantic cues derived from limited and unaligned text, resulting in SI-SDRi of 12.16 dB, SDRi of 12.66 dB, PESQi of 0.830 and STOIi of 0.150.

翻译：摘要：目标说话人提取旨在从音频混合信号中提取目标说话人的纯净语音，消除无关背景噪声和他人语音。尽管先前研究已探索了多种辅助线索，包括预录音频、视觉信息和空间信息，但在许多实际场景中获取和选择此类强线索并不可行。与此不同，本文基于从有限且非对齐文本内容（如演示幻灯片的摘要要点）中提取的语义线索来约束目标说话人提取算法。该方法特别适用于会议、海报展示或讲座演示等场景，这些场景下实时获取其他线索可能具有挑战性。为此，我们设计了两种不同的网络。具体而言，我们提出的文本提示提取网络将音频特征与基于内容的语义线索融合，以促进时频掩码生成，从而滤除无关噪声。实验结果表明，利用源自有限和非对齐文本的语义线索能够有效准确提取目标说话人语音，获得了12.16 dB的SI-SDRi、12.66 dB的SDRi、0.830的PESQi以及0.150的STOIi。