VoxBlink2: A 100K+ Speaker Recognition Corpus and the Open-Set Speaker-Identification Benchmark

In this paper, we provide a large audio-visual speaker recognition dataset, VoxBlink2, which includes approximately 10M utterances with videos from 110K+ speakers in the wild. This dataset represents a significant expansion over the VoxBlink dataset, encompassing a broader diversity of speakers and scenarios by the grace of an optimized data collection pipeline. Afterward, we explore the impact of training strategies, data scale, and model complexity on speaker verification and finally establish a new single-model state-of-the-art EER at 0.170% and minDCF at 0.006% on the VoxCeleb1-O test set. Such remarkable results motivate us to explore speaker recognition from a new challenging perspective. We raise the Open-Set Speaker-Identification task, which is designed to either match a probe utterance with a known gallery speaker or categorize it as an unknown query. Associated with this task, we design concrete benchmark and evaluation protocols. The data and model resources can be found in http://voxblink2.github.io.

The Database and Benchmark for Source Speaker Verification Against Voice Conversion

Voice conversion systems can transform audio to mimic another speaker's voice, thereby attacking speaker verification systems. However, ongoing studies on source speaker verification are hindered by limited data availability and methodological constraints. In this paper, we generate a large-scale converted speech database and train a batch of baseline systems based on the MFA-Conformer architecture to promote the source speaker verification task. In addition, we introduce a related task called conversion method recognition. An adapter-based multi-task learning approach is employed to achieve effective conversion method recognition without compromising source speaker verification performance. Additionally, we investigate and effectively address the open-set conversion method recognition problem through the implementation of an open-set nearest neighbor approach.

KunquDB: An Attempt for Speaker Verification in the Chinese Opera Scenario

This work aims to promote Chinese opera research in both musical and speech domains, with a primary focus on overcoming the data limitations. We introduce KunquDB, a relatively large-scale, well-annotated audio-visual dataset comprising 339 speakers and 128 hours of content. Originating from the Kunqu Opera Art Canon (Kunqu yishu dadian), KunquDB is meticulously structured by dialogue lines, providing explicit annotations including character names, speaker names, gender information, vocal manner classifications, and accompanied by preliminary text transcriptions. KunquDB provides a versatile foundation for role-centric acoustic studies and advancements in speech-related research, including Automatic Speaker Verification (ASV). Beyond enriching opera research, this dataset bridges the gap between artistic expression and technological innovation. Pioneering the exploration of ASV in Chinese opera, we construct four test trials considering two distinct vocal manners in opera voices: stage speech (ST) and singing (S). Implementing domain adaptation methods effectively mitigates domain mismatches induced by these vocal manner variations while there is still room for further improvement as a benchmark.

Multi-objective Progressive Clustering for Semi-supervised Domain Adaptation in Speaker Verification

Utilizing the pseudo-labeling algorithm with large-scale unlabeled data becomes crucial for semi-supervised domain adaptation in speaker verification tasks. In this paper, we propose a novel pseudo-labeling method named Multi-objective Progressive Clustering (MoPC), specifically designed for semi-supervised domain adaptation. Firstly, we utilize limited labeled data from the target domain to derive domain-specific descriptors based on multiple distinct objectives, namely within-graph denoising, intra-class denoising and inter-class denoising. Then, the Infomap algorithm is adopted for embedding clustering, and the descriptors are leveraged to further refine the target domain's pseudo-labels. Moreover, to further improve the quality of pseudo labels, we introduce the subcenter-purification and progressive-merging strategy for label denoising. Our proposed MoPC method achieves 4.95% EER and ranked the 1$^{st}$ place on the evaluation set of VoxSRC 2023 track 3. We also conduct additional experiments on the FFSVC dataset and yield promising results.

Haha-Pod: An Attempt for Laughter-based Non-Verbal Speaker Verification

It is widely acknowledged that discriminative representation for speaker verification can be extracted from verbal speech. However, how much speaker information that non-verbal vocalization carries is still a puzzle. This paper explores speaker verification based on the most ubiquitous form of non-verbal voice, laughter. First, we use a semi-automatic pipeline to collect a new Haha-Pod dataset from open-source podcast media. The dataset contains over 240 speakers' laughter clips with corresponding high-quality verbal speech. Second, we propose a Two-Stage Teacher-Student (2S-TS) framework to minimize the within-speaker embedding distance between verbal and non-verbal (laughter) signals. Considering Haha-Pod as a test set, two trials (S2L-Eval) are designed to verify the speaker's identity through laugh sounds. Experimental results demonstrate that our method can significantly improve the performance of the S2L-Eval test set with only a minor degradation on the VoxCeleb1 test set. The Haha-Pod dataset is open to access on https://drive.google.com/file/d/1J-HBRTsm_yWrcbkXupy-tiWRt5gE2LzG/view?usp=drive_link.

VoxBlink: X-Large Speaker Verification Dataset on Camera

In this paper, we contribute a novel and extensive dataset for speaker verification, which contains noisy 38k identities/1.45M utterances (VoxBlink) and relatively cleaned 18k identities/1.02M (VoxBlink-Clean) utterances for training. Firstly, we accumulate a 60K+ users' list with their avatars and download their short videos on YouTube. We then established an automatic and scalable pipeline to extract relevant speech and video segments from these videos. To our knowledge, the VoxBlink dataset is one of the largest speaker recognition datasets available. Secondly, we conduct a series of experiments based on different backbones trained on a mix of the VoxCeleb2 and the VoxBlink-Clean. Our findings highlight a notable performance improvement, ranging from 13% to 30%, across different backbone architectures upon integrating our dataset for training. The dataset will be made publicly available shortly.

The DKU-MSXF Speaker Verification System for the VoxCeleb Speaker Recognition Challenge 2023

This paper is the system description of the DKU-MSXF System for the track1, track2 and track3 of the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23). For Track 1, we utilize a network structure based on ResNet for training. By constructing a cross-age QMF training set, we achieve a substantial improvement in system performance. For Track 2, we inherite the pre-trained model from Track 1 and conducte mixed training by incorporating the VoxBlink-clean dataset. In comparison to Track 1, the models incorporating VoxBlink-clean data exhibit a performance improvement by more than 10% relatively. For Track3, the semi-supervised domain adaptation task, a novel pseudo-labeling method based on triple thresholds and sub-center purification is adopted to make domain adaptation. The final submission achieves mDCF of 0.1243 in task1, mDCF of 0.1165 in Track 2 and EER of 4.952% in Track 3.

The DKU-MSXF Diarization System for the VoxCeleb Speaker Recognition Challenge 2023

This paper describes the DKU-MSXF submission to track 4 of the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23). Our system pipeline contains voice activity detection, clustering-based diarization, overlapped speech detection, and target-speaker voice activity detection, where each procedure has a fused output from 3 sub-models. Finally, we fuse different clustering-based and TSVAD-based diarization systems using DOVER-Lap and achieve the 4.30% diarization error rate (DER), which ranks first place on track 4 of the challenge leaderboard.

Laugh Betrays You? Learning Robust Speaker Representation From Speech Containing Non-Verbal Fragments

The success of automatic speaker verification shows that discriminative speaker representations can be extracted from neutral speech. However, as a kind of non-verbal voice, laughter should also carry speaker information intuitively. Thus, this paper focuses on exploring speaker verification about utterances containing non-verbal laughter segments. We collect a set of clips with laughter components by conducting a laughter detection script on VoxCeleb and part of the CN-Celeb dataset. To further filter untrusted clips, probability scores are calculated by our binary laughter detection classifier, which is pre-trained by pure laughter and neutral speech. After that, based on the clips whose scores are over the threshold, we construct trials under two different evaluation scenarios: Laughter-Laughter (LL) and Speech-Laughter (SL). Then a novel method called Laughter-Splicing based Network (LSN) is proposed, which can significantly boost performance in both scenarios and maintain the performance on the neutral speech, such as the VoxCeleb1 test set. Specifically, our system achieves relative 20% and 22% improvement on Laughter-Laughter and Speech-Laughter trials, respectively. The meta-data and sample clips have been released at https://github.com/nevermoreLin/Laugh_LSN.

The DKU-Tencent System for the VoxCeleb Speaker Recognition Challenge 2022

This paper is the system description of the DKU-Tencent System for the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC22). In this challenge, we focus on track1 and track3. For track1, multiple backbone networks are adopted to extract frame-level features. Since track1 focus on the cross-age scenarios, we adopt the cross-age trials and perform QMF to calibrate score. The magnitude-based quality measures achieve a large improvement. For track3, the semi-supervised domain adaptation task, the pseudo label method is adopted to make domain adaptation. Considering the noise labels in clustering, the ArcFace is replaced by Sub-center ArcFace. The final submission achieves 0.107 mDCF in task1 and 7.135% EER in task3.