3 citations · 8 across the 17 of their papers we have counts for
19 papers
Qwen-Audio-3.1-ASR Technical Report
Chuanmeng Bian, Daren Chen, Peixin Chen +45
We present Qwen-Audio-3.1-ASR, a family of audio-language models that builds upon its predecesor Qwen-Audio-3.0-ASR with improved speech recognition and expanded capabilities in sp…
Multimodal Contrastive Representation Learning Based Voice Conversion for Electrolaryngeal Speech Enhancement
Ding Ma, Jinyi Mi, Jiajun He +3
Electrolaryngeal (EL) speech suffers from severe acoustic mismatches with normal speech, resulting in degraded naturalness and intelligibility. These mismatches also make sequence-…
Qwen-Audio-3.0-ASR Technical Report
Chuanmeng Bian, Daren Chen, Peixin Chen +42
In recent years, automatic speech recognition (ASR) has witnessed transformative advancements driven by three complementary paradigms: data scaling, model scaling, and deep integra…
Advancing Electrolaryngeal Speech Enhancement Through Speech-Text Representation Learning
Ding Ma, Jinyi Mi, Fengji Li +5
Objective: laryngectomees depend on an electromechanical device to generate electrolaryngeal (EL) speech. Compared with normal speech, EL speech suffers from severe distortion, lim…
VisionCreator: A Native Visual-Generation Agentic Model with Understanding, Thinking, Planning and Creation
Jinxiang Lai, Zexin Lu, Jiajun He +11
Visual content creation tasks demand a nuanced understanding of design conventions and creative workflows-capabilities challenging for general models, while workflow-based agents l…
Evaluating Large Language Models in Scientific Discovery
Zhangde Song, Jieyu Lu, Yuanqi Du +53
Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge and overlook the iterative reasonin…
BudgetLeak: Membership Inference Attacks on RAG Systems via the Generation Budget Side Channel
Hao Li, Jiajun He, Guangshuo Wang +3
Retrieval-Augmented Generation (RAG) enhances large language models by integrating external knowledge, but reliance on proprietary or sensitive corpora poses various data risks, in…
M4SER: Multimodal, Multirepresentation, Multitask, and Multistrategy Learning for Speech Emotion Recognition
Jiajun He, Xiaohan Shi, Cheng-Hung Hu +3
Multimodal speech emotion recognition (SER) has emerged as pivotal for improving human-machine interaction. Researchers are increasingly leveraging both speech and textual informat…
PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation
Jiajun He, Naoki Sawada, Koichi Miyazaki +1
Automatic speech recognition (ASR) systems struggle with domain-specific named entities, especially homophones. Contextual ASR improves recognition but often fails to capture fine-…
GIA-MIC: Multimodal Emotion Recognition with Gated Interactive Attention and Modality-Invariant Learning Constraints
Jiajun He, Jinyi Mi, Tomoki Toda
Multimodal emotion recognition (MER) extracts emotions from multimodal data, including visual, speech, and text inputs, playing a key role in human-computer interaction. Attention-…
CMT-LLM: Contextual Multi-Talker ASR Utilizing Large Language Models
Jiajun He, Naoki Sawada, Koichi Miyazaki +1
In real-world applications, automatic speech recognition (ASR) systems must handle overlapping speech from multiple speakers and recognize rare words like technical terms. Traditio…
PMF-CEC: Phoneme-augmented Multimodal Fusion for Context-aware ASR Error Correction with Error-specific Selective Decoding
Jiajun He, Tomoki Toda
End-to-end automatic speech recognition (ASR) models often struggle to accurately recognize rare words. Previously, we introduced an ASR postprocessing method called error detectio…
Two-stage Framework for Robust Speech Emotion Recognition Using Target Speaker Extraction in Human Speech Noise Conditions
Jinyi Mi, Xiaohan Shi, Ding Ma +3
Developing a robust speech emotion recognition (SER) system in noisy conditions faces challenges posed by different noise properties. Most previous studies have not considered the…
2DP-2MRC: 2-Dimensional Pointer-based Machine Reading Comprehension Method for Multimodal Moment Retrieval
Jiajun He, Tomoki Toda
Moment retrieval aims to locate the most relevant moment in an untrimmed video based on a given natural language query. Existing solutions can be roughly categorized into moment-ba…
Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding
Zhimin Li, Jianwei Zhang, Qin Lin +42
We present Hunyuan-DiT, a text-to-image diffusion transformer with fine-grained understanding of both English and Chinese. To construct Hunyuan-DiT, we carefully design the transfo…
MF-AED-AEC: Speech Emotion Recognition by Leveraging Multimodal Fusion, Asr Error Detection, and Asr Error Correction
Jiajun He, Xiaohan Shi, Xingfeng Li +1
The prevalent approach in speech emotion recognition (SER) involves integrating both audio and textual information to comprehensively identify the speaker's emotion, with the text…
A Comprehensive Study on the Effectiveness of ASR Representations for Noise-Robust Speech Emotion Recognition
Xiaohan Shi, Jiajun He, Xingfeng Li +1
This paper proposes an efficient attempt to noisy speech emotion recognition (NSER). Conventional NSER approaches have proven effective in mitigating the impact of artificial noise…
ed-cec: improving rare word recognition using asr postprocessing based on error detection and context-aware error correction
Jiajun He, Zekun Yang, Tomoki Toda
Automatic speech recognition (ASR) systems often encounter difficulties in accurately recognizing rare words, leading to errors that can have a negative impact on downstream tasks…
A New Entity Extraction Method Based on Machine Reading Comprehension
Xiaobo Jiang, Kun He, Jiajun He +1
Entity extraction is a key technology for obtaining information from massive texts in natural language processing. The further interaction between them does not meet the standards…