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.

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 collect a 60K+ users' list as well as their avatar and download their SHORT videos on the YouTube. Then, an automatically pipeline is devised to extract target user's speech segments and videos, which is efficient and scalable. To the best of our knowledge, the VoxBlink dataset is the largest speaker recognition dataset. Secondly, we develop a series of experiments based on VoxBlink-clean together with VoxCeleb2. Our findings highlight a notable improvement in performance, ranging from 15% to 30%, across different backbone architectures, upon integrating our dataset for training. The dataset will be released SOON~.

VoxSnap: 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 (VoxSnap) and relatively cleaned 18k identities/1.02M (VoxSnap-Clean) utterances for training. Firstly, we collect a 60K+ users' list as well as their avatar and download their SHORT videos on the YouTube. Then, an automatically pipeline is devised to extract target user's speech segments and videos, which is efficient and scalable. To the best of our knowledge, the VoxSnap dataset is the largest speaker recognition dataset. Secondly, we develop a series of experiments based on VoxSnap-clean together with VoxCeleb2. Our findings highlight a notable improvement in performance, ranging from 15% to 30%, across different backbone architectures, upon integrating our dataset for training. The dataset will be released SOON~.

Viia-hand: a Reach-and-grasp Restoration System Integrating Voice interaction, Computer vision and Auditory feedback for Blind Amputees

Visual feedback plays a crucial role in the process of amputation patients completing grasping in the field of prosthesis control. However, for blind and visually impaired (BVI) amputees, the loss of both visual and grasping abilities makes the "easy" reach-and-grasp task a feasible challenge. In this paper, we propose a novel multi-sensory prosthesis system helping BVI amputees with sensing, navigation and grasp operations. It combines modules of voice interaction, environmental perception, grasp guidance, collaborative control, and auditory/tactile feedback. In particular, the voice interaction module receives user instructions and invokes other functional modules according to the instructions. The environmental perception and grasp guidance module obtains environmental information through computer vision, and feedbacks the information to the user through auditory feedback modules (voice prompts and spatial sound sources) and tactile feedback modules (vibration stimulation). The prosthesis collaborative control module obtains the context information of the grasp guidance process and completes the collaborative control of grasp gestures and wrist angles of prosthesis in conjunction with the user's control intention in order to achieve stable grasp of various objects. This paper details a prototyping design (named viia-hand) and presents its preliminary experimental verification on healthy subjects completing specific reach-and-grasp tasks. Our results showed that, with the help of our new design, the subjects were able to achieve a precise reach and reliable grasp of the target objects in a relatively cluttered environment. Additionally, the system is extremely user-friendly, as users can quickly adapt to it with minimal training.

Hybrid Transformer and CNN Attention Network for Stereo Image Super-resolution

Multi-stage strategies are frequently employed in image restoration tasks. While transformer-based methods have exhibited high efficiency in single-image super-resolution tasks, they have not yet shown significant advantages over CNN-based methods in stereo super-resolution tasks. This can be attributed to two key factors: first, current single-image super-resolution transformers are unable to leverage the complementary stereo information during the process; second, the performance of transformers is typically reliant on sufficient data, which is absent in common stereo-image super-resolution algorithms. To address these issues, we propose a Hybrid Transformer and CNN Attention Network (HTCAN), which utilizes a transformer-based network for single-image enhancement and a CNN-based network for stereo information fusion. Furthermore, we employ a multi-patch training strategy and larger window sizes to activate more input pixels for super-resolution. We also revisit other advanced techniques, such as data augmentation, data ensemble, and model ensemble to reduce overfitting and data bias. Finally, our approach achieved a score of 23.90dB and emerged as the winner in Track 1 of the NTIRE 2023 Stereo Image Super-Resolution Challenge.

OPDN: Omnidirectional Position-aware Deformable Network for Omnidirectional Image Super-Resolution

360{\deg} omnidirectional images have gained research attention due to their immersive and interactive experience, particularly in AR/VR applications. However, they suffer from lower angular resolution due to being captured by fisheye lenses with the same sensor size for capturing planar images. To solve the above issues, we propose a two-stage framework for 360{\deg} omnidirectional image superresolution. The first stage employs two branches: model A, which incorporates omnidirectional position-aware deformable blocks (OPDB) and Fourier upsampling, and model B, which adds a spatial frequency fusion module (SFF) to model A. Model A aims to enhance the feature extraction ability of 360{\deg} image positional information, while Model B further focuses on the high-frequency information of 360{\deg} images. The second stage performs same-resolution enhancement based on the structure of model A with a pixel unshuffle operation. In addition, we collected data from YouTube to improve the fitting ability of the transformer, and created pseudo low-resolution images using a degradation network. Our proposed method achieves superior performance and wins the NTIRE 2023 challenge of 360{\deg} omnidirectional image super-resolution.

The DKU Post-Challenge Audio-Visual Wake Word Spotting System for the 2021 MISP Challenge: Deep Analysis

This paper further explores our previous wake word spotting system ranked 2-nd in Track 1 of the MISP Challenge 2021. First, we investigate a robust unimodal approach based on 3D and 2D convolution and adopt the simple attention module (SimAM) for our system to improve performance. Second, we explore different combinations of data augmentation methods for better performance. Finally, we study the fusion strategies, including score-level, cascaded and neural fusion. Our proposed multimodal system leverages multimodal features and uses the complementary visual information to mitigate the performance degradation of audio-only systems in complex acoustic scenarios. Our system obtains a false reject rate of 2.15% and a false alarm rate of 3.44% in the evaluation set of the competition database, which achieves the new state-of-the-art performance by 21% relative improvement compared to previous systems.

Target-Speaker Voice Activity Detection via Sequence-to-Sequence Prediction

Target-speaker voice activity detection is currently a promising approach for speaker diarization in complex acoustic environments. This paper presents a novel Sequence-to-Sequence Target-Speaker Voice Activity Detection (Seq2Seq-TSVAD) method that can efficiently address the joint modeling of large-scale speakers and predict high-resolution voice activities. Experimental results show that larger speaker capacity and higher output resolution can significantly reduce the diarization error rate (DER), which achieves the new state-of-the-art performance of 4.55% on the VoxConverse test set and 10.77% on Track 1 of the DIHARD-III evaluation set under the widely-used evaluation metrics.

GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene Flow

Scene flow is the collection of each point motion information in the 3D point clouds. It is a vital tool applied to many tasks, such as autonomous driving and augmented reality. However, there are always occlusion points between two consecutive point clouds, whether from the sparsity data sampling or real-world occlusion. In this paper, we focus on addressing occlusion issues in scene flow by self-similarity and local consistency of moving objects. We propose a GMA3D module based on the transformer framework, which utilizes local and global similarity to infer the motion information of occluded points from the motion information of local and global non-occluded points respectively, and then uses an offset generator to aggregate them. Our module is the first to apply the transformer-based architecture to gauge the scene flow occlusion problem on point clouds. Experiments show that our GMA3D can solve the occlusion problem in the scene flow, especially in the real scene. We evaluate the proposed method on the occluded version datasets and get state-of-the-art results on the real scene KITTI. To testify that GMA3D is still beneficial for non-occluded scene flow, we also conducted experiments on non-occluded version datasets and achieved state-of-the-art results on FlyThings3D and KITTI. The code is available at https://github.com/O-VIGIA/GMA3D.

The DKU-DukeECE Diarization System for the VoxCeleb Speaker Recognition Challenge 2022

This paper discribes the DKU-DukeECE submission to the 4th track of the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22). Our system contains a fused voice activity detection model, a clustering-based diarization model, and a target-speaker voice activity detection-based overlap detection model. Overall, the submitted system is similar to our previous year's system in VoxSRC-21. The difference is that we use a much better speaker embedding and a fused voice activity detection, which significantly improves the performance. Finally, we fuse 4 different systems using DOVER-lap and achieve 4.75 of the diarization error rate, which ranks the 1st place in track 4.