15 papers
AffectDF: The Most Comprehensive Benchmark for Speech Deepfake Detection against Emotionally Expressive Attacks
Aurosweta Mahapatra, Xiutian Zhao, Shreeram Suresh Chandra +7
Speech deepfake detection (SDD) systems achieve strong performance on conventional benchmarks; however, existing datasets provide limited coverage of emotionally expressive and rec…
TurnFSM for Full-Duplex Dialogue System: Internalizing State-Machine Logic for Streaming Semantic Voice Activity Detection and Utterance-Level Rejection
Zhiwei Lin, Tianjiao Du, Qiaochu Huang +6
Full-duplex voice assistants must continuously listen while speaking, handling user interruptions under low-latency and resource-constrained streaming conditions. Existing end-to-e…
LSZone: A Lightweight Spatial Information Modeling Architecture for Real-time In-car Multi-zone Speech Separation
Jun Chen, Shichao Hu, Jiuxin Lin +8
In-car multi-zone speech separation, which captures voices from different speech zones, plays a crucial role in human-vehicle interaction. Although previous SpatialNet has achieved…
MeanFlowSE: One-Step Generative Speech Enhancement via MeanFlow
Yike Zhu, Boyi Kang, Ziqian Wang +6
Speech enhancement (SE) recovers clean speech from noisy signals and is vital for applications such as telecommunications and automatic speech recognition (ASR). While generative a…
Easy Turn: Integrating Acoustic and Linguistic Modalities for Robust Turn-Taking in Full-Duplex Spoken Dialogue Systems
Guojian Li, Chengyou Wang, Hongfei Xue +8
Full-duplex interaction is crucial for natural human-machine communication, yet remains challenging as it requires robust turn-taking detection to decide when the system should spe…
SenSE: Semantic-Aware High-Fidelity Universal Speech Enhancement
Xingchen Li, Hanke Xie, Ziqian Wang +4
Generative Universal Speech Enhancement (USE) methods aim to leverage generative models to improve speech quality under various types of distortions. However, existing generative s…
CabinSep: IR-Augmented Mask-Based MVDR for Real-Time In-Car Speech Separation with Distributed Heterogeneous Arrays
Runduo Han, Yanxin Hu, Yihui Fu +4
Separating overlapping speech from multiple speakers is crucial for effective human-vehicle interaction. This paper proposes CabinSep, a lightweight neural mask-based minimum varia…
EchoFree: Towards Ultra Lightweight and Efficient Neural Acoustic Echo Cancellation
Xingchen Li, Boyi Kang, Ziqian Wang +4
In recent years, neural networks (NNs) have been widely applied in acoustic echo cancellation (AEC). However, existing approaches struggle to meet real-world low-latency and comput…
AISHELL-5: The First Open-Source In-Car Multi-Channel Multi-Speaker Speech Dataset for Automatic Speech Diarization and Recognition
Yuhang Dai, He Wang, Xingchen Li +8
This paper delineates AISHELL-5, the first open-source in-car multi-channel multi-speaker Mandarin automatic speech recognition (ASR) dataset. AISHLL-5 includes two parts: (1) over…
LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement
Boyi Kang, Xinfa Zhu, Zihan Zhang +10
Recent advancements in language models (LMs) have demonstrated strong capabilities in semantic understanding and contextual modeling, which have flourished in generative speech enh…
Distil-DCCRN: A Small-footprint DCCRN Leveraging Feature-based Knowledge Distillation in Speech Enhancement
Runduo Han, Weiming Xu, Zihan Zhang +2
The deep complex convolution recurrent network (DCCRN) achieves excellent speech enhancement performance by utilizing the audio spectrum's complex features. However, it has a large…
BS-PLCNet 2: Two-stage Band-split Packet Loss Concealment Network with Intra-model Knowledge Distillation
Zihan Zhang, Xianjun Xia, Chuanzeng Huang +2
Audio packet loss is an inevitable problem in real-time speech communication. A band-split packet loss concealment network (BS-PLCNet) targeting full-band signals was recently prop…
BS-PLCNet: Band-split Packet Loss Concealment Network with Multi-task Learning Framework and Multi-discriminators
Zihan Zhang, Jiayao Sun, Xianjun Xia +3
Packet loss is a common and unavoidable problem in voice over internet phone (VoIP) systems. To deal with the problem, we propose a band-split packet loss concealment network (BS-P…
SELM: Speech Enhancement Using Discrete Tokens and Language Models
Ziqian Wang, Xinfa Zhu, Zihan Zhang +4
Language models (LMs) have shown superior performances in various speech generation tasks recently, demonstrating their powerful ability for semantic context modeling. Given the in…
An Exploration of Task-decoupling on Two-stage Neural Post Filter for Real-time Personalized Acoustic Echo Cancellation
Zihan Zhang, Jiayao Sun, Xianjun Xia +4
Deep learning based techniques have been popularly adopted in acoustic echo cancellation (AEC). Utilization of speaker representation has extended the frontier of AEC, thus attract…