论文标题
改善带有音频嵌入的无线电节目的自动分割
Improving automated segmentation of radio shows with audio embeddings
论文作者
论文摘要
已证明音频功能可用于提高自动化主题细分系统的性能。这项研究探讨了使用音频嵌入进行自动化的无线电节目局部分割的新任务。我们使用来自不同域的三个数据集上的多类分类任务创建了三个不同的音频嵌入生成器。我们评估了音频嵌入的主题细分性能，并将其与仅文本基线进行比较。我们发现，包括通过非语音声音事件分类任务生成的音频嵌入的设置大大优于我们的文本基线，在F1量中的基线比32.3％。此外，我们发现不同的分类任务会产生在分割性能中变化的音频嵌入。
Audio features have been proven useful for increasing the performance of automated topic segmentation systems. This study explores the novel task of using audio embeddings for automated, topically coherent segmentation of radio shows. We created three different audio embedding generators using multi-class classification tasks on three datasets from different domains. We evaluate topic segmentation performance of the audio embeddings and compare it against a text-only baseline. We find that a set-up including audio embeddings generated through a non-speech sound event classification task significantly outperforms our text-only baseline by 32.3% in F1-measure. In addition, we find that different classification tasks yield audio embeddings that vary in segmentation performance.
