Thanks to recent advancements in end-to-end speech modeling technology, it
has become increasingly feasible to imitate and clone a user`s voice. This
leads to a significant challenge in differentiating between authentic and
fabricated audio segments. To address the issue of user voice abuse and misuse,
the second Audio Deepfake Detection Challenge (ADD 2023) aims to detect and
analyze deepfake speech utterances. Specifically, Track 2, named the
Manipulation Region Location (RL), aims to pinpoint the location of manipulated
regions in audio, which can be present in both real and generated audio
segments. We propose our novel TranssionADD system as a solution to the
challenging problem of model robustness and audio segment outliers in the trace
competition. Our system provides three unique contributions: 1) we adapt
sequence tagging task for audio deepfake detection; 2) we improve model
generalization by various data augmentation techniques; 3) we incorporate
multi-frame detection (MFD) module to overcome limited representation provided
by a single frame and use isolated-frame penalty (IFP) loss to handle outliers
in segments. Our best submission achieved 2nd place in Track 2, demonstrating
the effectiveness and robustness of our proposed system.

本研究提出了一种 TranssionADD 系统，通过序列标签任务和 MFD 模块结合多种数据增强技术改进模型能力，使用 IFP 损失函数和处理 segment 异常值，有效解决了检测深度伪造语音 utterance 的难题。

TranssionADD: 基于多帧强化学习的序列标注模型用于音频深度伪造检测

TranssionADD: A multi-frame reinforcement based sequence tagging model  for audio deepfake detection

While current 3D object recognition research mostly focuses on the real-time,
onboard scenario, there are many offboard use cases of perception that are
largely under-explored, such as using machines to automatically generate
high-quality 3D labels. Existing 3D object detectors fail to satisfy the
high-quality requirement for offboard uses due to the limited input and speed
constraints. In this paper, we propose a novel offboard 3D object detection
pipeline using point cloud sequence data. Observing that different frames
capture complementary views of objects, we design the offboard detector to make
use of the temporal points through both multi-frame object detection and novel
object-centric refinement models. Evaluated on the Waymo Open Dataset, our
pipeline named 3D Auto Labeling shows significant gains compared to the
state-of-the-art onboard detectors and our offboard baselines. Its performance
is even on par with human labels verified through a human label study. Further
experiments demonstrate the application of auto labels for semi-supervised
learning and provide extensive analysis to validate various design choices.

本文提出了一种使用点云序列数据的离线三维对象检测方法，通过多帧对象检测和新颖的对象中心细化模型利用时间点，实现了高质量的自动化三维对象标注，比当前先进的在线检测器表现更优秀，甚至接近人类标注。