1 citations · 2 across the 17 of their papers we have counts for
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MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models
He Zhang, Wenqian Cui, Haoning Xu +5
Full-Duplex Speech Language Models (FD-SLMs) enable real-time, overlapping conversational interactions, offering a more dynamic user experience compared to traditional half-duplex…
MOPSA: Mixture of Prompt-Experts Based Speaker Adaptation for Elderly Speech Recognition
Chengxi Deng, Xurong Xie, Shujie Hu +10
This paper proposes a novel Mixture of Prompt-Experts based Speaker Adaptation approach (MOPSA) for elderly speech recognition. It allows zero-shot, real-time adaptation to unseen…
Towards LLM-Empowered Fine-Grained Speech Descriptors for Explainable Emotion Recognition
Youjun Chen, Xurong Xie, Haoning Xu +6
This paper presents a novel end-to-end LLM-empowered explainable speech emotion recognition (SER) approach. Fine-grained speech emotion descriptor (SED) features, e.g., pitch, tone…
Effective and Efficient One-pass Compression of Speech Foundation Models Using Sparsity-aware Self-pinching Gates
Haoning Xu, Zhaoqing Li, Youjun Chen +5
This paper presents a novel approach for speech foundation models compression that tightly integrates model pruning and parameter update into a single stage. Highly compact layer-l…
Towards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic Precision
Zhaoqing Li, Haoning Xu, Zengrui Jin +7
Model compression has become an emerging need as the sizes of modern speech systems rapidly increase. In this paper, we study model weight quantization, which directly reduces the…
Unfolding A Few Structures for The Many: Memory-Efficient Compression of Conformer and Speech Foundation Models
Zhaoqing Li, Haoning Xu, Xurong Xie +3
This paper presents a novel memory-efficient model compression approach for Conformer ASR and speech foundation systems. Our approach features a unique "small-to-large" design. A c…
Effective and Efficient Mixed Precision Quantization of Speech Foundation Models
Haoning Xu, Zhaoqing Li, Zengrui Jin +7
This paper presents a novel mixed-precision quantization approach for speech foundation models that tightly integrates mixed-precision learning and quantized model parameter estima…
Phone-purity Guided Discrete Tokens for Dysarthric Speech Recognition
Huimeng Wang, Xurong Xie, Mengzhe Geng +6
Discrete tokens extracted provide efficient and domain adaptable speech features. Their application to disordered speech that exhibits articulation imprecision and large mismatch a…