Search Results for author:
Found 50 papers, 16 papers with code
Multimodal Fusion with Pre-Trained Model Features in Affective Behaviour Analysis In-the-wild
Multimodal fusion is a significant method for most multimodal tasks.
Can Deception Detection Go Deeper? Dataset, Evaluation, and Benchmark for Deception Reasoning
To address data scarcity, this paper proposes a new data collection pipeline.
Progressive Distillation Based on Masked Generation Feature Method for Knowledge Graph Completion
This paper proposes a progressive distillation method based on masked generation features for KGC task, aiming to significantly reduce the complexity of pre-trained models.
HiCMAE: Hierarchical Contrastive Masked Autoencoder for Self-Supervised Audio-Visual Emotion Recognition
Audio-Visual Emotion Recognition (AVER) has garnered increasing attention in recent years for its critical role in creating emotion-ware intelligent machines.
Ranked #3 on
Dynamic Facial Expression Recognition
on MAFW
Contrastive Learning
Dynamic Facial Expression Recognition
+3
SVFAP: Self-supervised Video Facial Affect Perceiver
Video-based facial affect analysis has recently attracted increasing attention owing to its critical role in human-computer interaction.
Ranked #3 on
Dynamic Facial Expression Recognition
on FERV39k
Dynamic Facial Expression Recognition
Emotion Recognition
+2
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
The rapid evolution of speech synthesis and voice conversion has raised substantial concerns due to the potential misuse of such technology, prompting a pressing need for effective audio deepfake detection mechanisms.
GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition
To bridge this gap, we present the quantitative evaluation results of GPT-4V on 21 benchmark datasets covering 6 tasks: visual sentiment analysis, tweet sentiment analysis, micro-expression recognition, facial emotion recognition, dynamic facial emotion recognition, and multimodal emotion recognition.
DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection
Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals.
Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio Detection
The orthogonal weight modification to overcome catastrophic forgetting does not consider the similarity of genuine audio across different datasets.
MAE-DFER: Efficient Masked Autoencoder for Self-supervised Dynamic Facial Expression Recognition
Dynamic facial expression recognition (DFER) is essential to the development of intelligent and empathetic machines.
Ranked #2 on
Dynamic Facial Expression Recognition
on FERV39k
Dynamic Facial Expression Recognition
Facial Expression Recognition
Low-rank Adaptation Method for Wav2vec2-based Fake Audio Detection
Self-supervised speech models are a rapidly developing research topic in fake audio detection.
Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion
When decreasing the number of sampling steps (i. e., the number of line segments used to fit the path), the ease of fitting straight lines compared to curves allows us to generate higher quality samples from a random noise with fewer iterations.
Adaptive Fake Audio Detection with Low-Rank Model Squeezing
During the inference stage, these adaptation matrices are combined with the existing model to generate the final prediction output.
M2-CTTS: End-to-End Multi-scale Multi-modal Conversational Text-to-Speech Synthesis
However, it is still a challenge to comprehensively model the conversation, and a majority of conversational TTS systems only focus on extracting global information and omit local prosody features, which contain important fine-grained information like keywords and emphasis.
MER 2023: Multi-label Learning, Modality Robustness, and Semi-Supervised Learning
The first Multimodal Emotion Recognition Challenge (MER 2023) was successfully held at ACM Multimedia.
UnifySpeech: A Unified Framework for Zero-shot Text-to-Speech and Voice Conversion
Text-to-speech (TTS) and voice conversion (VC) are two different tasks both aiming at generating high quality speaking voice according to different input modality.
Emotion Selectable End-to-End Text-based Speech Editing
To achieve this task, we propose Emo-CampNet (emotion CampNet), which can provide the option of emotional attributes for the generated speech in text-based speech editing and has the one-shot ability to edit unseen speakers' speech.
SceneFake: An Initial Dataset and Benchmarks for Scene Fake Audio Detection
Some scene fake audio detection benchmark results on the SceneFake dataset are reported in this paper.
IRNet: Iterative Refinement Network for Noisy Partial Label Learning
In this paper, we relax this assumption and focus on a more general problem, noisy PLL, where the ground-truth label may not exist in the candidate set.
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era
Speech is the fundamental mode of human communication, and its synthesis has long been a core priority in human-computer interaction research.
System Fingerprint Recognition for Deepfake Audio: An Initial Dataset and Investigation
The rapid progress of deep speech synthesis models has posed significant threats to society such as malicious content manipulation.
An Initial Investigation for Detecting Vocoder Fingerprints of Fake Audio
Many effective attempts have been made for fake audio detection.
Fully Automated End-to-End Fake Audio Detection
The existing fake audio detection systems often rely on expert experience to design the acoustic features or manually design the hyperparameters of the network structure.
Efficient Multimodal Transformer with Dual-Level Feature Restoration for Robust Multimodal Sentiment Analysis
With the proliferation of user-generated online videos, Multimodal Sentiment Analysis (MSA) has attracted increasing attention recently.
Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features
Meanwhile, to make full use of the phase and full-band information, we also propose to use real and imaginary spectrogram features as complementary input features and model the disjoint subbands separately.
Two-Aspect Information Fusion Model For ABAW4 Multi-task Challenge
In this paper, we propose the solution to the Multi-Task Learning (MTL) Challenge of the 4th Affective Behavior Analysis in-the-wild (ABAW) competition.
Adaptive Pseudo-Siamese Policy Network for Temporal Knowledge Prediction
Specifically, we design the policy network in our model as a pseudo-siamese policy network that consists of two sub-policy networks.
EmotionNAS: Two-stream Neural Architecture Search for Speech Emotion Recognition
Speech emotion recognition (SER) is an important research topic in human-computer interaction.
NeuralDPS: Neural Deterministic Plus Stochastic Model with Multiband Excitation for Noise-Controllable Waveform Generation
We have also verified through experiments that this method can effectively control the noise components in the predicted speech and adjust the SNR of speech.
GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversation
To this end, we propose a novel framework for incomplete multimodal learning in conversations, called "Graph Complete Network (GCNet)", filling the gap of existing works.
CampNet: Context-Aware Mask Prediction for End-to-End Text-Based Speech Editing
It can solve unnatural prosody in the edited region and synthesize the speech corresponding to the unseen words in the transcript.
MixKG: Mixing for harder negative samples in knowledge graph
The representations of entities and relations are learned via contrasting the positive and negative triplets.
ADD 2022: the First Audio Deep Synthesis Detection Challenge
Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021.
Singing-Tacotron: Global duration control attention and dynamic filter for End-to-end singing voice synthesis
Firstly, we propose a global duration control attention mechanism for the SVS model.
Reducing language context confusion for end-to-end code-switching automatic speech recognition
We propose a language-related attention mechanism to reduce multilingual context confusion for the E2E code-switching ASR model based on the Equivalence Constraint (EC) Theory.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Knowledge graph enhanced recommender system
This is attributed to the rich attribute information contained in KG to improve item and user representations as side information.
Multi-Level Graph Contrastive Learning
In this paper, we propose a Multi-Level Graph Contrastive Learning (MLGCL) framework for learning robust representation of graph data by contrasting space views of graphs.
Continual Learning for Fake Audio Detection
However, fine-tuning leads to performance degradation on previous data.
Half-Truth: A Partially Fake Audio Detection Dataset
Therefore, this paper develops such a dataset for half-truth audio detection (HAD).
FSR: Accelerating the Inference Process of Transducer-Based Models by Applying Fast-Skip Regularization
It takes a lot of computation and time to predict the blank tokens, but only the non-blank tokens will appear in the final output sequence.
TSNAT: Two-Step Non-Autoregressvie Transformer Models for Speech Recognition
To address these two problems, we propose a new model named the two-step non-autoregressive transformer(TSNAT), which improves the performance and accelerating the convergence of the NAR model by learning prior knowledge from a parameters-sharing AR model.
Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERT
Based on this idea, we propose a non-autoregressive speech recognition model called LASO (Listen Attentively, and Spell Once).
Tucker decomposition-based Temporal Knowledge Graph Completion
Developing the model for temporal knowledge graphs completion is an increasingly important task.
Deep Time Delay Neural Network for Speech Enhancement with Full Data Learning
This paper proposes a deep time delay neural network (TDNN) for speech enhancement with full data learning.
Self-supervised Graph Representation Learning via Bootstrapping
In addition, we summarize three kinds of augmentation methods for graph-structured data and apply them to the DGB.
Gated Recurrent Fusion with Joint Training Framework for Robust End-to-End Speech Recognition
The joint training framework for speech enhancement and recognition methods have obtained quite good performances for robust end-to-end automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
One In A Hundred: Select The Best Predicted Sequence from Numerous Candidates for Streaming Speech Recognition
Inspired by the success of two-pass end-to-end models, we introduce a transformer decoder and the two-stage inference method into the streaming CTC model.
Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition
Emotion recognition remains a complex task due to speaker variations and low-resource training samples.
Ranked #1 on
Speech Emotion Recognition
on IEMOCAP
(using extra training data)
Decoupling Pronunciation and Language for End-to-end Code-switching Automatic Speech Recognition
In this paper, we propose a decoupled transformer model to use monolingual paired data and unpaired text data to alleviate the problem of code-switching data shortage.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Deep imitator: Handwriting calligraphy imitation via deep attention networks
Calligraphy imitation (CI) from a handful of target handwriting samples is such a challenging task that most of the existing writing style analysis or handwriting generation methods do not exhibit satisfactory performance.
