Search Results for author:
Found 61 papers, 8 papers with code
PFID: Privacy First Inference Delegation Framework for LLMs
Our framework was designed to be communication efficient, computation can be delegated to the local client so that the server's computation burden can be lightened.
RREH: Reconstruction Relations Embedded Hashing for Semi-Paired Cross-Modal Retrieval
For unpaired data, to effectively capture the latent discriminative features, the high-order relationships between unpaired data and anchors are embedded into the latent subspace, which are computed by efficient linear reconstruction.
Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning
Previous ERC methods relied on simple connections for cross-modal fusion and ignored the information differences between modalities, resulting in the model being unable to focus on modality-specific emotional information.
Transformer in Touch: A Survey
The Transformer model, initially achieving significant success in the field of natural language processing, has recently shown great potential in the application of tactile perception.
Potential and Limitations of LLMs in Capturing Structured Semantics: A Case Study on SRL
Large Language Models (LLMs) play a crucial role in capturing structured semantics to enhance language understanding, improve interpretability, and reduce bias.
QLSC: A Query Latent Semantic Calibrator for Robust Extractive Question Answering
Extractive Question Answering (EQA) in Machine Reading Comprehension (MRC) often faces the challenge of dealing with semantically identical but format-variant inputs.
Extractive Question-Answering
Machine Reading Comprehension
+1
Towards Comprehensive Multimodal Perception: Introducing the Touch-Language-Vision Dataset
Tactility provides crucial support and enhancement for the perception and interaction capabilities of both humans and robots.
Medical Speech Symptoms Classification via Disentangled Representation
After combining the intent from two domains into a joint representation, the integrated intent representation is fed into a decision layer for classification.
Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning
Data filtering for instruction tuning has proved important in improving both the efficiency and performance of the tuning process.
Leveraging Biases in Large Language Models: "bias-kNN'' for Effective Few-Shot Learning
Large Language Models (LLMs) have shown significant promise in various applications, including zero-shot and few-shot learning.
EmoTalker: Emotionally Editable Talking Face Generation via Diffusion Model
In recent years, the field of talking faces generation has attracted considerable attention, with certain methods adept at generating virtual faces that convincingly imitate human expressions.
ED-TTS: Multi-Scale Emotion Modeling using Cross-Domain Emotion Diarization for Emotional Speech Synthesis
We introduce ED-TTS, a multi-scale emotional speech synthesis model that leverages Speech Emotion Diarization (SED) and Speech Emotion Recognition (SER) to model emotions at different levels.
CP-EB: Talking Face Generation with Controllable Pose and Eye Blinking Embedding
This paper proposes a talking face generation method named "CP-EB" that takes an audio signal as input and a person image as reference, to synthesize a photo-realistic people talking video with head poses controlled by a short video clip and proper eye blinking embedding.
PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter
The Retrieval Question Answering (ReQA) task employs the retrieval-augmented framework, composed of a retriever and generator.
An In-depth Survey of Large Language Model-based Artificial Intelligence Agents
Due to the powerful capabilities demonstrated by large language model (LLM), there has been a recent surge in efforts to integrate them with AI agents to enhance their performance.
AOSR-Net: All-in-One Sandstorm Removal Network
Most existing sandstorm image enhancement methods are based on traditional theory and prior knowledge, which often restrict their applicability in real-world scenarios.
DiffTalker: Co-driven audio-image diffusion for talking faces via intermediate landmarks
Generating realistic talking faces is a complex and widely discussed task with numerous applications.
Machine Unlearning Methodology base on Stochastic Teacher Network
The rise of the phenomenon of the "right to be forgotten" has prompted research on machine unlearning, which grants data owners the right to actively withdraw data that has been used for model training, and requires the elimination of the contribution of that data to the model.
From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
In the realm of Large Language Models (LLMs), the balance between instruction data quality and quantity is a focal point.
Prompt Guided Copy Mechanism for Conversational Question Answering
Conversational Question Answering (CQA) is a challenging task that aims to generate natural answers for conversational flow questions.
Boosting Chinese ASR Error Correction with Dynamic Error Scaling Mechanism
Chinese Automatic Speech Recognition (ASR) error correction presents significant challenges due to the Chinese language's unique features, including a large character set and borderless, morpheme-based structure.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
CollabKG: A Learnable Human-Machine-Cooperative Information Extraction Toolkit for (Event) Knowledge Graph Construction
In order to construct or extend entity-centric and event-centric knowledge graphs (KG and EKG), the information extraction (IE) annotation toolkit is essential.
On the Calibration and Uncertainty with Pólya-Gamma Augmentation for Dialog Retrieval Models
Deep neural retrieval models have amply demonstrated their power but estimating the reliability of their predictions remains challenging.
Efficient Uncertainty Estimation with Gaussian Process for Reliable Dialog Response Retrieval
Deep neural networks have achieved remarkable performance in retrieval-based dialogue systems, but they are shown to be ill calibrated.
QI-TTS: Questioning Intonation Control for Emotional Speech Synthesis
Recent expressive text to speech (TTS) models focus on synthesizing emotional speech, but some fine-grained styles such as intonation are neglected.
Dynamic Alignment Mask CTC: Improved Mask-CTC with Aligned Cross Entropy
Because of predicting all the target tokens in parallel, the non-autoregressive models greatly improve the decoding efficiency of speech recognition compared with traditional autoregressive models.
Improving EEG-based Emotion Recognition by Fusing Time-frequency And Spatial Representations
Using deep learning methods to classify EEG signals can accurately identify people's emotions.
ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT
Zero-shot information extraction (IE) aims to build IE systems from the unannotated text.
Linguistic-Enhanced Transformer with CTC Embedding for Speech Recognition
We also find that in joint CTC-Attention ASR model, decoder is more sensitive to linguistic information than acoustic information.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Adapitch: Adaption Multi-Speaker Text-to-Speech Conditioned on Pitch Disentangling with Untranscribed Data
In this paper, we proposed Adapitch, a multi-speaker TTS method that makes adaptation of the supervised module with untranscribed data.
Semi-Supervised Learning Based on Reference Model for Low-resource TTS
Most previous neural text-to-speech (TTS) methods are mainly based on supervised learning methods, which means they depend on a large training dataset and hard to achieve comparable performance under low-resource conditions.
Improving Imbalanced Text Classification with Dynamic Curriculum Learning
Recent advances in pre-trained language models have improved the performance for text classification tasks.
Improving Speech Representation Learning via Speech-level and Phoneme-level Masking Approach
In this work, we proposed two kinds of masking approaches: (1) speech-level masking, making the model to mask more speech segments than silence segments, (2) phoneme-level masking, forcing the model to mask the whole frames of the phoneme, instead of phoneme pieces.
MetaSpeech: Speech Effects Switch Along with Environment for Metaverse
Metaverse expands the physical world to a new dimension, and the physical environment and Metaverse environment can be directly connected and entered.
Pre-Avatar: An Automatic Presentation Generation Framework Leveraging Talking Avatar
Since the beginning of the COVID-19 pandemic, remote conferencing and school-teaching have become important tools.
Blur the Linguistic Boundary: Interpreting Chinese Buddhist Sutra in English via Neural Machine Translation
Buddhism is an influential religion with a long-standing history and profound philosophy.
Boosting Star-GANs for Voice Conversion with Contrastive Discriminator
Nonparallel multi-domain voice conversion methods such as the StarGAN-VCs have been widely applied in many scenarios.
Speech Representation Disentanglement with Adversarial Mutual Information Learning for One-shot Voice Conversion
One-shot voice conversion (VC) with only a single target speaker's speech for reference has become a hot research topic.
TGAVC: Improving Autoencoder Voice Conversion with Text-Guided and Adversarial Training
In this paper, a novel voice conversion framework, named $\boldsymbol T$ext $\boldsymbol G$uided $\boldsymbol A$utoVC(TGAVC), is proposed to more effectively separate content and timbre from speech, where an expected content embedding produced based on the text transcriptions is designed to guide the extraction of voice content.
Uncertainty Calibration for Deep Audio Classifiers
In this work, we investigate the uncertainty calibration for deep audio classifiers.
Adaptive Activation Network For Low Resource Multilingual Speech Recognition
The existing models mostly established a bottleneck (BN) layer by pre-training on a large source language, and transferring to the low resource target language.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Speech Augmentation Based Unsupervised Learning for Keyword Spotting
In our experiments, with augmentation based unsupervised learning, our KWS model achieves better performance than other unsupervised methods, such as CPC, APC, and MPC.
Self-Attention for Incomplete Utterance Rewriting
In this paper, we propose a novel method by directly extracting the coreference and omission relationship from the self-attention weight matrix of the transformer instead of word embeddings and edit the original text accordingly to generate the complete utterance.
VU-BERT: A Unified framework for Visual Dialog
The visual dialog task attempts to train an agent to answer multi-turn questions given an image, which requires the deep understanding of interactions between the image and dialog history.
ClsVC: Learning Speech Representations with two different classification tasks.
Voice conversion(VC) aims to convert one speaker's voice to generate a new speech as it is said by another speaker.
Speech2Video: Cross-Modal Distillation for Speech to Video Generation
This paper investigates a novel task of talking face video generation solely from speeches.
Loss Prediction: End-to-End Active Learning Approach For Speech Recognition
End-to-end speech recognition systems usually require huge amounts of labeling resource, while annotating the speech data is complicated and expensive.
Dropout Regularization for Self-Supervised Learning of Transformer Encoder Speech Representation
We evaluated the proposed methods on phoneme classification and speaker recognition tasks.
Unidirectional Memory-Self-Attention Transducer for Online Speech Recognition
We propose a novel network structure, called Memory-Self-Attention (MSA) Transducer.
Joint Intent Detection And Slot Filling Based on Continual Learning Model
Slot filling and intent detection have become a significant theme in the field of natural language understanding.
Applying Wav2vec2.0 to Speech Recognition in Various Low-resource Languages
To verify its universality over languages, we apply pre-trained models to solve low-resource speech recognition tasks in various spoken languages.
GraphPB: Graphical Representations of Prosody Boundary in Speech Synthesis
Graph-to-sequence model is proposed and formed by a graph encoder and an attentional decoder.
MelGlow: Efficient Waveform Generative Network Based on Location-Variable Convolution
In this paper, an efficient network, named location-variable convolution, is proposed to model the dependencies of waveforms.
A Real-time Robot-based Auxiliary System for Risk Evaluation of COVID-19 Infection
The structure of our model are maintained concise to be implemented for real-time applications.
MLNET: An Adaptive Multiple Receptive-field Attention Neural Network for Voice Activity Detection
The MLNET leveraged multi-branches to extract multiple contextual speech information and investigated an effective attention block to weight the most crucial parts of the context for final classification.
Large-scale Transfer Learning for Low-resource Spoken Language Understanding
However, the increased complexity of a model can also introduce high risk of over-fitting, which is a major challenge in SLU tasks due to the limitation of available data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Prosody Learning Mechanism for Speech Synthesis System Without Text Length Limit
Recent neural speech synthesis systems have gradually focused on the control of prosody to improve the quality of synthesized speech, but they rarely consider the variability of prosody and the correlation between prosody and semantics together.
Integration of Automatic Sentence Segmentation and Lexical Analysis of Ancient Chinese based on BiLSTM-CRF Model
The basic tasks of ancient Chinese information processing include automatic sentence segmentation, word segmentation, part-of-speech tagging and named entity recognition.
MDCNN-SID: Multi-scale Dilated Convolution Network for Singer Identification
Most singer identification methods are processed in the frequency domain, which potentially leads to information loss during the spectral transformation.
AlignTTS: Efficient Feed-Forward Text-to-Speech System without Explicit Alignment
Targeting at both high efficiency and performance, we propose AlignTTS to predict the mel-spectrum in parallel.
GraphTTS: graph-to-sequence modelling in neural text-to-speech
This paper leverages the graph-to-sequence method in neural text-to-speech (GraphTTS), which maps the graph embedding of the input sequence to spectrograms.
