2 citations · 4 across the 11 of their papers we have counts for
12 papers
Dissecting Hierarchical Reasoning Models: A Mechanistic Study
Leo Raphael Rodrigues, Jian Kang
We study Hierarchical Reasoning Model (HRM), a representative hierarchical Transformer-based latent reasoning model with many variants, on Sudoku, Maze, and ARC-AGI-2. We mechanist…
DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement
Cunhang Fan, Enrui Liu, Jing Zhou +6
Although artificial neural network (ANN) based speech enhancement (SE) methods demonstrate excellent performance, the high computational complexity and high energy consumption hind…
Audio-Cogito: Towards Deep Audio Reasoning in Large Audio Language Models
Longhao Li, Hongjie Chen, Zehan Li +5
Recent advances in reasoning models have driven significant progress in text and multimodal domains, yet audio reasoning remains relatively limited. Only a few Large Audio Language…
Lost in Adaptation: Layer-Selective Recovery of Temporal Reasoning in Video-Language Models
Zihang Fu, Haonan Wang, Jian Kang +2
Multimodal adaptation can weaken temporal reasoning (TR) in video-language models (VLMs) even when the visual evidence needed for an answer is correctly perceived. This weakened co…
A Unified Spoken Language Model with Injected Emotional-Attribution Thinking for Human-like Interaction
Qing Wang, Zehan Li, Yaodong Song +6
This paper presents a unified spoken language model for emotional intelligence, enhanced by a novel data construction strategy termed Injected Emotional-Attribution Thinking (IEAT)…
WenetSpeech-Chuan: A Large-Scale Sichuanese Corpus with Rich Annotation for Dialectal Speech Processing
Yuhang Dai, Ziyu Zhang, Shuai Wang +13
The scarcity of large-scale, open-source data for dialects severely hinders progress in speech technology, a challenge particularly acute for the widely spoken Sichuanese dialects…
WenetSpeech-Yue: A Large-scale Cantonese Speech Corpus with Multi-dimensional Annotation
Longhao Li, Zhao Guo, Hongjie Chen +15
The development of speech understanding and generation has been significantly accelerated by the availability of large-scale, high-quality speech datasets. Among these, ASR and TTS…
GOAT-SLM: A Spoken Language Model with Paralinguistic and Speaker Characteristic Awareness
Hongjie Chen, Zehan Li, Yaodong Song +13
Recent advances in end-to-end spoken language models (SLMs) have significantly improved the ability of AI systems to engage in natural spoken interactions. However, most existing m…
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…
TELEVAL: A Benchmark Designed for Spoken Language Models in Chinese Interactive Scenarios
Zehan Li, Hongjie Chen, Qing Wang +9
Spoken Language Models (SLMs) are expected to support natural spoken interaction beyond task completion. However, existing SLM benchmarks primarily evaluate semantic correctness in…
Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis
Tianyi Xu, Hongjie Chen, Wang Qing +6
Large-scale training corpora have significantly improved the performance of ASR models. Unfortunately, due to the relative scarcity of data, Chinese accents and dialects remain a c…
GOAT-TTS: Expressive and Realistic Speech Generation via A Dual-Branch LLM
Yaodong Song, Hongjie Chen, Jie Lian +8
While large language models (LLMs) have revolutionized text-to-speech (TTS) synthesis through discrete tokenization paradigms, current architectures exhibit fundamental tensions be…