3 citations · 7 across the 26 of their papers we have counts for
16 papers · 1 filter
Speech-XL: Towards Long-Form Speech Understanding in Large Speech Language Models
Haoqin Sun, Chenyang Lyu, Shiwan Zhao +5
Despite the growing success of Large Speech Language Models (LSLMs) in processing short-term acoustic signals, their extension to long-form audio understanding is severely bottlene…
SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation
Hui Wang, Jinghua Zhao, Yifan Yang +9
Generative speech technologies are progressing rapidly, but evaluating the perceptual quality of synthetic speech remains a core challenge. Existing methods typically rely on scala…
AudioEval: Automatic Dual-Perspective and Multi-Dimensional Evaluation of Text-to-Audio-Generation
Hui Wang, Jinghua Zhao, Junyang Cheng +5
Text-to-audio (TTA) generation is advancing rapidly, but evaluation remains challenging because human listening studies are expensive and existing automatic metrics capture only li…
WildElder: A Chinese Elderly Speech Dataset from the Wild with Fine-Grained Manual Annotations
Hui Wang, Jiaming Zhou, Jiabei He +2
Elderly speech poses unique challenges for automatic processing due to age-related changes such as slower articulation and vocal tremors. Existing Chinese datasets are mostly recor…
MECap-R1: Emotion-aware Policy with Reinforcement Learning for Multimodal Emotion Captioning
Haoqin Sun, Chenyang Lyu, Xiangyu Kong +9
Speech Emotion Captioning (SEC) has emerged as a notable research direction. The inherent complexity of emotional content in human speech makes it challenging for traditional discr…
Omni-CLST: Error-aware Curriculum Learning with guided Selective chain-of-Thought for audio question answering
Jinghua Zhao, Hang Su, Lichun Fan +4
With the rapid progress of large audio-language models (LALMs), audio question answering (AQA) has emerged as a challenging task requiring both fine-grained audio understanding and…
TTA-Bench: A Comprehensive Benchmark for Evaluating Text-to-Audio Models
Hui Wang, Cheng Liu, Junyang Chen +7
Text-to-Audio (TTA) generation has made rapid progress, but current evaluation methods remain narrow, focusing mainly on perceptual quality while overlooking robustness, generaliza…
DIFFA: Large Language Diffusion Models Can Listen and Understand
Jiaming Zhou, Hongjie Chen, Shiwan Zhao +9
Recent advances in large language models (LLMs) have shown remarkable capabilities across textual and multimodal domains. In parallel, diffusion-based language models have emerged…
StreamMel: Real-Time Zero-shot Text-to-Speech via Interleaved Continuous Autoregressive Modeling
Hui Wang, Yifan Yang, Shujie Liu +7
Recent advances in zero-shot text-to-speech (TTS) synthesis have achieved high-quality speech generation for unseen speakers, but most systems remain unsuitable for real-time appli…
RA-CLAP: Relation-Augmented Emotional Speaking Style Contrastive Language-Audio Pretraining For Speech Retrieval
Haoqin Sun, Jingguang Tian, Jiaming Zhou +8
The Contrastive Language-Audio Pretraining (CLAP) model has demonstrated excellent performance in general audio description-related tasks, such as audio retrieval. However, in the…
Discrete Audio Representations for Automated Audio Captioning
Jingguang Tian, Haoqin Sun, Xinhui Hu +1
Discrete audio representations, termed audio tokens, are broadly categorized into semantic and acoustic tokens, typically generated through unsupervised tokenization of continuous…
MusicEval: A Generative Music Dataset with Expert Ratings for Automatic Text-to-Music Evaluation
Cheng Liu, Hui Wang, Jinghua Zhao +6
The technology for generating music from textual descriptions has seen rapid advancements. However, evaluating text-to-music (TTM) systems remains a significant challenge, primaril…
ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5
Jiaming Zhou, Shiyao Wang, Shiwan Zhao +10
Automatic speech recognition (ASR) systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0 and HuBERT. However,…
M2R-Whisper: Multi-stage and Multi-scale Retrieval Augmentation for Enhancing Whisper
Jiaming Zhou, Shiwan Zhao, Jiabei He +6
State-of-the-art models like OpenAI's Whisper exhibit strong performance in multilingual automatic speech recognition (ASR), but they still face challenges in accurately recognizin…
Iterative Prototype Refinement for Ambiguous Speech Emotion Recognition
Haoqin Sun, Shiwan Zhao, Xiangyu Kong +4
Recognizing emotions from speech is a daunting task due to the subtlety and ambiguity of expressions. Traditional speech emotion recognition (SER) systems, which typically rely on…
Fine-grained Disentangled Representation Learning for Multimodal Emotion Recognition
Haoqin Sun, Shiwan Zhao, Xuechen Wang +3
Multimodal emotion recognition (MMER) is an active research field that aims to accurately recognize human emotions by fusing multiple perceptual modalities. However, inherent heter…