Speaker diarization is the task of determining “who spoke when?” in an audio or video
recording that contains an unknown amount of speech and also an unknown number of …

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …

The objective of this work is speaker recognition under noisy and unconstrained conditions.
We make two key contributions. First, we introduce a very large-scale audio-visual dataset …

This paper introduces the SAMSum Corpus, a new dataset with abstractive dialogue
summaries. We investigate the challenges it poses for automated summarization by testing …

While the significant advancements have made in the generation of deepfakes using deep
learning technologies, its misuse is a well-known issue now. Deepfakes can cause severe …

Most existing datasets for speaker identification contain samples obtained under quite
constrained conditions, and are usually hand-annotated, hence limited in size. The goal of …

We introduce Wavesplit, an end-to-end source separation system. From a single mixture, the
model infers a representation for each source and then estimates each source signal given …

Semantic processing is a fundamental research domain in computational linguistics. In the
era of powerful pre-trained language models and large language models, the advancement …

This project evaluates the accuracy of YouTube's automatically-generated captions across
two genders and five dialect groups. Speakers' dialect and gender was controlled for by …

MediaSum, a large-scale media interview dataset consisting of 463.6 K transcripts with
abstractive summaries. To create this dataset, we collect interview transcripts from NPR and …