In the field of speaker verification, session or channel variability poses a significant challenge. While many contemporary methods aim to disentangle session information from speaker embeddings, we introduce a novel approach using an additional embedding to represent the session information. This is achieved by training an auxiliary network appended to the speaker embedding extractor which remains fixed in this training process. This results in two similarity scores: one for the speakers information and one for the session information. The latter score acts as a compensator for the former that might be skewed due to session variations. Our extensive experiments demonstrate that session information can be effectively compensated without retraining of the embedding extractor.

The goal of this work is Active Speaker Detection (ASD), a task to determine whether a person is speaking or not in a series of video frames. Previous works have dealt with the task by exploring network architectures while learning effective representations has been less explored. In this work, we propose TalkNCE, a novel talk-aware contrastive loss. The loss is only applied to part of the full segments where a person on the screen is actually speaking. This encourages the model to learn effective representations through the natural correspondence of speech and facial movements. Our loss can be jointly optimized with the existing objectives for training ASD models without the need for additional supervision or training data. The experiments demonstrate that our loss can be easily integrated into the existing ASD frameworks, improving their performance. Our method achieves state-of-the-art performances on AVA-ActiveSpeaker and ASW datasets.

This paper presents a multi-modal dataset that contains rich transcriptions of spoken conversations. As diverse multi-modal and multi-task models emerge, there is a growing need for multi-modal training and evaluation datasets accompanied by rich metadata. However, there is no universal dataset that addresses these requirements for the diverse tasks partially due to the cost of annotation. To overcome this limitation, we develop a semi-automatic pipeline that makes the annotation more feasible. The resulting dataset is VoxMM, a multi-modal, multi-domain dataset. VoxMM incorporates video, audio, and text modalities. In terms of labels, it offers a wide array of metadata such as speaker labels, transcriptions, gender, and more. VoxMM supports both the training and the evaluation of any-to-any modality mapping models. It also offers a more accurate representation of real-world scenarios, bridging the gap between controlled laboratory experiments and the varying performances in the real-world. We present initial benchmarks on automatic speech recognition and speaker diarisation. The VoxMM dataset can be downloaded from https://mm.kaist.ac.kr/projects/voxmm

Automatic speech recognition (ASR) via call is essential for various applications, including AI for contact center (AICC) services. Despite the advancement of ASR, however, most publicly available call-based speech corpora such as Switchboard are old-fashioned. Also, most existing call corpora are in English and mainly focus on open domain dialog or general scenarios such as audiobooks. Here we introduce a new large-scale Korean call-based speech corpus under a goal-oriented dialog scenario from more than 11,000 people, i.e., ClovaCall corpus. ClovaCall includes approximately 60,000 pairs of a short sentence and its corresponding spoken utterance in a restaurant reservation domain. We validate the effectiveness of our dataset with intensive experiments using two standard ASR models. Furthermore, we release our ClovaCall dataset and baseline source codes to be available via https://github.com/ClovaAI/ClovaCall. Copyright © 2020 ISCA