14 papers
AD-Mamba: Architecture-Complementary Knowledge Distillation for Efficient and Robust Audio Classification
Junyu Wang, Tianrui Wang, Meng Ge +3
Recent advancements in large-scale audio classification have been primarily driven by Transformer-based architectures due to their strong capacity in modeling long-range dependenci…
EmoS: A Theory-Grounded Framework for Evaluating and Aligning Emotional Intelligence in Spoken Language Models
Junyu Wang, Siyuan Zhang, Peiyuan Jiang +11
Despite significant advances in instruction-following and auditory comprehension, the evaluation of Emotional Intelligence (EI) in Spoken Language Models (SLMs) remains confined to…
Evaluating the Expressive Appropriateness of Speech in Rich Contexts
Tianrui Wang, Ziyang Ma, Yizhou Peng +26
Evaluating expressive speech remains challenging, as existing methods mainly assess emotional intensity and overlook whether a speech sample is expressively appropriate for its con…
MSR-HuBERT: Self-supervised Pre-training for Adaptation to Multiple Sampling Rates
Zikang Huang, Meng Ge, Tianrui Wang +4
Self-supervised learning (SSL) has advanced speech processing. However, existing speech SSL methods typically assume a single sampling rate and struggle with mixed-rate data due to…
POTSA: A Cross-Lingual Speech Alignment Framework for Speech-to-Text Translation
Xuanchen Li, Chenrui Cui, Tianrui Wang +9
Speech Large Language Models have achieved breakthroughs in multilingual speech-to-text translation. However, existing approaches often overlook semantic commonalities across sourc…
Learning Time-Graph Frequency Representation for Monaural Speech Enhancement
Tingting Wang, Tianrui Wang, Meng Ge +2
The Graph Fourier Transform (GFT) has recently demonstrated promising results in speech enhancement. However, existing GFT-based speech enhancement approaches often employ fixed gr…
LORT: Locally Refined Convolution and Taylor Transformer for Monaural Speech Enhancement
Junyu Wang, Zizhen Lin, Tianrui Wang +3
Achieving superior enhancement performance while maintaining a low parameter count and computational complexity remains a challenge in the field of speech enhancement. In this pape…
Pay More Attention To Audio: Mitigating Imbalance of Cross-Modal Attention in Large Audio Language Models
Junyu Wang, Ziyang Ma, Zhengding Luo +4
Large Audio-Language Models (LALMs) often suffer from audio-textual attention imbalance, prioritizing text over acoustic information, particularly in the multi-modal fusion layers…
Word-Level Emotional Expression Control in Zero-Shot Text-to-Speech Synthesis
Tianrui Wang, Haoyu Wang, Meng Ge +10
While emotional text-to-speech (TTS) has made significant progress, most existing research remains limited to utterance-level emotional expression and fails to support word-level c…
ASDA: Audio Spectrogram Differential Attention Mechanism for Self-Supervised Representation Learning
Junyu Wang, Tianrui Wang, Meng Ge +2
In recent advancements in audio self-supervised representation learning, the standard Transformer architecture has emerged as the predominant approach, yet its attention mechanism…
Reducing the Gap Between Pretrained Speech Enhancement and Recognition Models Using a Real Speech-Trained Bridging Module
Zhongjian Cui, Chenrui Cui, Tianrui Wang +6
The information loss or distortion caused by single-channel speech enhancement (SE) harms the performance of automatic speech recognition (ASR). Observation addition (OA) is an eff…
Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement
Junyu Wang, Zizhen Lin, Tianrui Wang +3
In recent speech enhancement (SE) research, transformer and its variants have emerged as the predominant methodologies. However, the quadratic complexity of the self-attention mech…
Efficient Emotion and Speaker Adaptation in LLM-Based TTS via Characteristic-Specific Partial Fine-Tuning
Tianrui Wang, Meng Ge, Cheng Gong +11
While LLM-based TTS models exhibit zero-shot emotion and speaker cloning, their cloning fidelity and pronunciation clarity degrade on unseen domains. Fine-tuning is essential for a…
Time-Graph Frequency Representation with Singular Value Decomposition for Neural Speech Enhancement
Tingting Wang, Tianrui Wang, Meng Ge +3
Time-frequency (T-F) domain methods for monaural speech enhancement have benefited from the success of deep learning. Recently, focus has been put on designing two-stream network m…