Search Results for author:
Found 25 papers, 7 papers with code
CrowNER at Rocling 2022 Shared Task: NER using MacBERT and Adversarial Training
This study uses training and validation data from the “ROCLING 2022 Chinese Health Care Named Entity Recognition Task” for modeling.
Towards Generalized Source Tracing for Codec-Based Deepfake Speech
Recent attempts at source tracing for codec-based deepfake speech (CodecFake), generated by neural audio codec-based speech generation (CoSG) models, have exhibited suboptimal performance.
Knowledge Retrieval Based on Generative AI
Using TTQA and TMMLU+ as evaluation datasets, the system employs BGE-M3 for dense vector retrieval to obtain highly relevant search results and BGE-reranker to reorder these results based on query relevance.
Improving Location-based Thermal Emission Side-Channel Analysis Using Iterative Transfer Learning
This paper proposes the use of iterative transfer learning applied to deep learning models for side-channel attacks.
BLAPose: Enhancing 3D Human Pose Estimation with Bone Length Adjustment
Our results demonstrate that existing 3D human pose estimation models can be significantly enhanced through this adjustment process.
Improving Real-Time Music Accompaniment Separation with MMDenseNet
Source-to-distortion ratio, real-time factor, and optimal latency are employed to evaluate the performance.
Neural Codec-based Adversarial Sample Detection for Speaker Verification
In this paper, we propose a neural codec-based adversarial sample detection method for ASV.
Singing Voice Graph Modeling for SingFake Detection
Detecting singing voice deepfakes, or SingFake, involves determining the authenticity and copyright of a singing voice.
EMO-SUPERB: An In-depth Look at Speech Emotion Recognition
Speech emotion recognition (SER) is a pivotal technology for human-computer interaction systems.
Novel Preprocessing Technique for Data Embedding in Engineering Code Generation Using Large Language Model
We present four main contributions to enhance the performance of Large Language Models (LLMs) in generating domain-specific code: (i) utilizing LLM-based data splitting and data renovation techniques to improve the semantic representation of embeddings' space; (ii) introducing the Chain of Density for Renovation Credibility (CoDRC), driven by LLMs, and the Adaptive Text Renovation (ATR) algorithm for assessing data renovation reliability; (iii) developing the Implicit Knowledge Expansion and Contemplation (IKEC) Prompt technique; and (iv) effectively refactoring existing scripts to generate new and high-quality scripts with LLMs.
Adapting pretrained speech model for Mandarin lyrics transcription and alignment
With the use of data augmentation and source separation model, results show that the proposed method achieves a character error rate of less than 18% on a Mandarin polyphonic dataset for lyrics transcription, and a mean absolute error of 0. 071 seconds for lyrics alignment.
WC-SBERT: Zero-Shot Text Classification via SBERT with Self-Training for Wikipedia Categories
To achieve this objective, we propose a novel self-training strategy that uses labels rather than text for training, significantly reducing the model's training time.
Personalized Audio Quality Preference Prediction
A siamese network is used to compare the inputs and predict the preference.
Multimodal Transformer Distillation for Audio-Visual Synchronization
This paper proposed an MTDVocaLiST model, which is trained by our proposed multimodal Transformer distillation (MTD) loss.
Push-Pull: Characterizing the Adversarial Robustness for Audio-Visual Active Speaker Detection
Audio-visual active speaker detection (AVASD) is well-developed, and now is an indispensable front-end for several multi-modal applications.
Adversarial Speaker Distillation for Countermeasure Model on Automatic Speaker Verification
The countermeasure (CM) model is developed to protect ASV systems from spoof attacks and prevent resulting personal information leakage in Automatic Speaker Verification (ASV) system.
Learning to match transient sound events using attentional similarity for few-shot sound recognition
In this paper, we introduce a novel attentional similarity module for the problem of few-shot sound recognition.
Sound Audio and Speech Processing
SVSGAN: Singing Voice Separation via Generative Adversarial Network
Separating two sources from an audio mixture is an important task with many applications.
