5 citations · 5 across the 19 of their papers we have counts for
20 papers
Long-Tail Rebalancing for Non-Verbal Vocalization-Aware ASR: A Track 1 System for the NVVSpeech Challenge
Shangyue Jia, Jingru Ma, Yangzhuo Li +9
Non-verbal vocalizations (NVVs) carry important paralinguistic information but are often omitted by conventional automatic speech recognition (ASR) systems. The ISCSLP NVVSpeech Ch…
The ISCSLP 2026 Real-World Audio-Visual Speech Enhancement Challenge: A Benchmark with Natural Mixtures and Degraded Video
Kai Li, Wenze Ren, Junjie Li +11
Audio-visual speech enhancement (AVSE) uses a target speaker's visible articulation to recover that speaker's voice from overlapped speech. Yet most evaluation protocols rely on sy…
Learning to Prefer Reliably: Error-Augmented Emotion Preference Optimization with Calibrated Fusion
Zilong Huang, Junyi Peng, Junjie Li +5
Emotion preference learning uses pairwise comparisons between candidate descriptions to align multimodal large language models (MLLMs) with human judgments of open-ended emotion de…
RRP-Voice: A Longitudinal Dataset and Benchmark for Recurrent Respiratory Papillomatosis Detection
Wenze Ren, Ke-Han Lu, Kai-Wei Chang +8
Deep learning has advanced pathological voice detection rapidly, yet rare laryngeal diseases remain underexplored due to data scarcity. Recurrent Respiratory Papillomatosis (RRP) e…
How Auditory Knowledge in LLM Backbones Shapes Audio Language Models: A Holistic Evaluation
Ke-Han Lu, Szu-Wei Fu, Chao-Han Huck Yang +13
Large language models (LLMs) have been widely used as knowledge backbones of Large Audio Language Models (LALMs), yet how much auditory knowledge they encode through text-only pre-…
MOS-Bias: From Hidden Gender Bias to Gender-Aware Speech Quality Assessment
Wenze Ren, Yi-Cheng Lin, Wen-Chin Huang +5
The Mean Opinion Score (MOS) serves as the standard metric for speech quality assessment, yet biases in human annotations remain underexplored. We conduct the first systematic anal…
TaigiSpeech: A Low-Resource Real-World Speech Intent Dataset and Preliminary Results with Scalable Data Mining In-the-Wild
Kai-Wei Chang, Yi-Cheng Lin, Huang-Cheng Chou +9
Speech technologies have advanced rapidly and serve diverse populations worldwide. However, many languages remain underrepresented due to limited resources. In this paper, we intro…
TW-Sound580K: A Regional Audio-Text Dataset with Verification-Guided Curation for Localized Audio-Language Modeling
Hao-Hui Xie, Ho-Lam Chung, Yi-Cheng Lin +4
Large Audio-Language Models (LALMs) typically struggle with localized dialectal prosody due to the scarcity of specialized corpora. We present TW-Sound580K, a Taiwanese audio-text…
Leveraging Mamba with Full-Face Vision for Audio-Visual Speech Enhancement
Rong Chao, Wenze Ren, You-Jin Li +5
Recent Mamba-based models have shown promise in speech enhancement by efficiently modeling long-range temporal dependencies. However, models like Speech Enhancement Mamba (SEMamba)…
Game-Time: Evaluating Temporal Dynamics in Spoken Language Models
Kai-Wei Chang, En-Pei Hu, Chun-Yi Kuan +7
Conversational Spoken Language Models (SLMs) are emerging as a promising paradigm for real-time speech interaction. However, their capacity of temporal dynamics, including the abil…
DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment
Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu +25
We introduce DeSTA2.5-Audio, a general-purpose Large Audio Language Model (LALM) designed for robust auditory perception and instruction-following. Recent LALMs augment Large Langu…
HighRateMOS: Sampling-Rate Aware Modeling for Speech Quality Assessment
Wenze Ren, Yi-Cheng Lin, Wen-Chin Huang +9
Modern speech quality prediction models are trained on audio data resampled to a specific sampling rate. When faced with higher-rate audio at test time, these models can produce bi…
ToxicTone: A Mandarin Audio Dataset Annotated for Toxicity and Toxic Utterance Tonality
Yu-Xiang Luo, Yi-Cheng Lin, Ming-To Chuang +9
Despite extensive research on toxic speech detection in text, a critical gap remains in handling spoken Mandarin audio. The lack of annotated datasets that capture the unique proso…
CodecFake+: Codec-Based Resynthesized Data as a Proxy for Detecting CodecFake Speech
Xuanjun Chen, Jiawei Du, Haibin Wu +8
With the rapid advancement of neural audio codecs, codec-based speech generation (CoSG) systems have become highly powerful. Unfortunately, CoSG also enables the creation of highly…
Robust Audio-Visual Speech Enhancement: Correcting Misassignments in Complex Environments with Advanced Post-Processing
Wenze Ren, Kuo-Hsuan Hung, Rong Chao +3
This paper addresses the prevalent issue of incorrect speech output in audio-visual speech enhancement (AVSE) systems, which is often caused by poor video quality and mismatched tr…
MC-SEMamba: A Simple Multi-channel Extension of SEMamba
Wen-Yuan Ting, Wenze Ren, Rong Chao +3
Transformer-based models have become increasingly popular and have impacted speech-processing research owing to their exceptional performance in sequence modeling. Recently, a prom…
DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset
Jiawei Du, I-Ming Lin, I-Hsiang Chiu +6
Mainstream zero-shot TTS production systems like Voicebox and Seed-TTS achieve human parity speech by leveraging Flow-matching and Diffusion models, respectively. Unfortunately, hu…
Leveraging Joint Spectral and Spatial Learning with MAMBA for Multichannel Speech Enhancement
Wenze Ren, Haibin Wu, Yi-Cheng Lin +7
In multichannel speech enhancement, effectively capturing spatial and spectral information across different microphones is crucial for noise reduction. Traditional methods, such as…
EMO-Codec: An In-Depth Look at Emotion Preservation capacity of Legacy and Neural Codec Models With Subjective and Objective Evaluations
Wenze Ren, Yi-Cheng Lin, Huang-Cheng Chou +5
The neural codec model reduces speech data transmission delay and serves as the foundational tokenizer for speech language models (speech LMs). Preserving emotional information in…
Deep Complex U-Net with Conformer for Audio-Visual Speech Enhancement
Shafique Ahmed, Chia-Wei Chen, Wenze Ren +7
Recent studies have increasingly acknowledged the advantages of incorporating visual data into speech enhancement (SE) systems. In this paper, we introduce a novel audio-visual SE…