7 citations · 10 across the 12 of their papers we have counts for
13 papers
XingChen-UniFE: A Unified Generative Speech Front-End via Complementary Acoustic-Semantic Modeling
Kelan Kuang, Qihan Hu, Jian Kang +3
This paper presents XingChen-UniFE, a unified large audio language model (LALM) for general speech restoration (GSR) and target speaker extraction (TSE). The proposed model employs…
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…
Tibetan-TTS:Low-Resource Tibetan Speech Synthesis with Large Model Adaptation
Jiaxu He, Chao Wang, Jie Lian +4
Tibetan text-to-speech (TTS) has long been challenged by scarce speech resources, significant dialectal variation, and the complex mapping between written text and spoken pronuncia…
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…
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)…
Multilingual Speech Recognition Using Discrete Tokens with a Two-step Training Strategy
Zehan Li, Yan Yang, Xueqing Li +3
Pre-trained models, especially self-supervised learning (SSL) models, have demonstrated impressive results in automatic speech recognition (ASR) task. While most applications of SS…
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…
Bridging the Gap between Continuous and Informative Discrete Representations by Random Product Quantization
Xueqing Li, Hao Ma, Zehan Li +8
Self-supervised learning (SSL) has become a core technique in speech processing, but the high dimensionality of its representations makes discretization essential for improving eff…
Advancing GenAI Assisted Programming--A Comparative Study on Prompt Efficiency and Code Quality Between GPT-4 and GLM-4
Angus Yang, Zehan Li, Jie Li
This study aims to explore the best practices for utilizing GenAI as a programming tool, through a comparative analysis between GPT-4 and GLM-4. By evaluating prompting strategies…