57 citations · 384 across the 43 of their papers we have counts for
43 papers
Delusions of Large Language Models
Hongshen Xu, Zixv yang, Zichen Zhu +7
Large Language Models often generate factually incorrect but plausible outputs, known as hallucinations. We identify a more insidious phenomenon, LLM delusion, defined as high beli…
Unified Pathological Speech Analysis with Prompt Tuning
Fei Yang, Xuenan Xu, Mengyue Wu +1
Pathological speech analysis has been of interest in the detection of certain diseases like depression and Alzheimer's disease and attracts much interest from researchers. However,…
Long Term Memory: The Foundation of AI Self-Evolution
Xun Jiang, Feng Li, Han Zhao +12
Large language models (LLMs) like GPTs, trained on vast datasets, have demonstrated impressive capabilities in language understanding, reasoning, and planning, achieving human-leve…
DiveSound: LLM-Assisted Automatic Taxonomy Construction for Diverse Audio Generation
Baihan Li, Zeyu Xie, Xuenan Xu +5
Audio generation has attracted significant attention. Despite remarkable enhancement in audio quality, existing models overlook diversity evaluation. This is partially due to the l…
PicoAudio: Enabling Precise Timestamp and Frequency Controllability of Audio Events in Text-to-audio Generation
Zeyu Xie, Xuenan Xu, Zhizheng Wu +1
Recently, audio generation tasks have attracted considerable research interests. Precise temporal controllability is essential to integrate audio generation with real applications.…
AudioTime: A Temporally-aligned Audio-text Benchmark Dataset
Zeyu Xie, Xuenan Xu, Zhizheng Wu +1
Recent advancements in audio generation have enabled the creation of high-fidelity audio clips from free-form textual descriptions. However, temporal relationships, a critical feat…
FakeSound: Deepfake General Audio Detection
Zeyu Xie, Baihan Li, Xuenan Xu +3
With the advancement of audio generation, generative models can produce highly realistic audios. However, the proliferation of deepfake general audio can pose negative consequences…
Evaluation of data inconsistency for multi-modal sentiment analysis
Yufei Wang, Mengyue Wu
Emotion semantic inconsistency is an ubiquitous challenge in multi-modal sentiment analysis (MSA). MSA involves analyzing sentiment expressed across various modalities like text, a…
SemantiCodec: An Ultra Low Bitrate Semantic Audio Codec for General Sound
Haohe Liu, Xuenan Xu, Yi Yuan +3
Large language models (LLMs) have significantly advanced audio processing through audio codecs that convert audio into discrete tokens, enabling the application of language modelli…
A Detailed Audio-Text Data Simulation Pipeline using Single-Event Sounds
Xuenan Xu, Xiaohang Xu, Zeyu Xie +3
Recently, there has been an increasing focus on audio-text cross-modal learning. However, most of the existing audio-text datasets contain only simple descriptions of sound events.…
Enhancing Audio Generation Diversity with Visual Information
Zeyu Xie, Baihan Li, Xuenan Xu +2
Audio and sound generation has garnered significant attention in recent years, with a primary focus on improving the quality of generated audios. However, there has been limited re…
Towards Weakly Supervised Text-to-Audio Grounding
Xuenan Xu, Ziyang Ma, Mengyue Wu +1
Text-to-audio grounding (TAG) task aims to predict the onsets and offsets of sound events described by natural language. This task can facilitate applications such as multimodal in…
Towards Lexical Analysis of Dog Vocalizations via Online Videos
Yufei Wang, Chunhao Zhang, Jieyi Huang +2
Deciphering the semantics of animal language has been a grand challenge. This study presents a data-driven investigation into the semantics of dog vocalizations via correlating dif…
Does My Dog ''Speak'' Like Me? The Acoustic Correlation between Pet Dogs and Their Human Owners
Jieyi Huang, Chunhao Zhang, Yufei Wang +2
How hosts language influence their pets' vocalization is an interesting yet underexplored problem. This paper presents a preliminary investigation into the possible correlation bet…
Auto-ACD: A Large-scale Dataset for Audio-Language Representation Learning
Luoyi Sun, Xuenan Xu, Mengyue Wu +1
Recently, the AI community has made significant strides in developing powerful foundation models, driven by large-scale multimodal datasets. However, for audio representation learn…
Improving Audio Caption Fluency with Automatic Error Correction
Hanxue Zhang, Zeyu Xie, Xuenan Xu +2
Automated audio captioning (AAC) is an important cross-modality translation task, aiming at generating descriptions for audio clips. However, captions generated by previous AAC mod…
Enhance Temporal Relations in Audio Captioning with Sound Event Detection
Zeyu Xie, Xuenan Xu, Mengyue Wu +1
Automated audio captioning aims at generating natural language descriptions for given audio clips, not only detecting and classifying sounds, but also summarizing the relationships…
LLM-empowered Chatbots for Psychiatrist and Patient Simulation: Application and Evaluation
Siyuan Chen, Mengyue Wu, Kenny Q. Zhu +3
Empowering chatbots in the field of mental health is receiving increasing amount of attention, while there still lacks exploration in developing and evaluating chatbots in psychiat…
Diverse and Vivid Sound Generation from Text Descriptions
Guangwei Li, Xuenan Xu, Lingfeng Dai +2
Previous audio generation mainly focuses on specified sound classes such as speech or music, whose form and content are greatly restricted. In this paper, we go beyond specific aud…
Semantic Space Grounded Weighted Decoding for Multi-Attribute Controllable Dialogue Generation
Zhiling Zhang, Mengyue Wu, Kenny Q. Zhu
Controlling chatbot utterance generation with multiple attributes such as personalities, emotions and dialogue acts is a practically useful but under-studied problem. We propose a…
BLAT: Bootstrapping Language-Audio Pre-training based on AudioSet Tag-guided Synthetic Data
Xuenan Xu, Zhiling Zhang, Zelin Zhou +4
Compared with ample visual-text pre-training research, few works explore audio-text pre-training, mostly due to the lack of sufficient parallel audio-text data. Most existing metho…
OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue
Zhi Chen, Yuncong Liu, Lu Chen +3
This paper presents an ontology-aware pretrained language model (OPAL) for end-to-end task-oriented dialogue (TOD). Unlike chit-chat dialogue models, task-oriented dialogue models…
Symptom Identification for Interpretable Detection of Multiple Mental Disorders
Zhiling Zhang, Siyuan Chen, Mengyue Wu +1
Mental disease detection (MDD) from social media has suffered from poor generalizability and interpretability, due to lack of symptom modeling. This paper introduces PsySym, the fi…
Psychiatric Scale Guided Risky Post Screening for Early Detection of Depression
Zhiling Zhang, Siyuan Chen, Mengyue Wu +1
Depression is a prominent health challenge to the world, and early risk detection (ERD) of depression from online posts can be a promising technique for combating the threat. Early…