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arXiv · 2610.11150

SmoothConv and DuplexConv: Complementary Mandarin Multi-Party Conversational Speech Corpora for Speech Interaction

Chengyou Wang·Mingchen Shao·Chunjiang He·Zeyu Zhu·Jierui Guo·Bingshen Mu·Zikai Liu·Hanke Xie·Yuhang Dai·Zhou Zhu·Lei Xie
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Abstract

Recent advances in large audio language models (LALMs) have driven the development of natural and intelligent speech interaction systems. Such systems need to model complex conversational behaviors, including turn-taking, overlapping speech, and speaker coordination. Multi-party conversations provide a realistic setting for studying these behaviors, yet existing Mandarin conversational corpora often lack synchronized participant-level speech tracks and comprehensive annotations. In this work, we introduce SmoothConv and DuplexConv, two complementary Mandarin multi-party conversational speech corpora totaling 2,100 hours. SmoothConv provides human-verified conversations for reliable analysis and evaluation, while DuplexConv offers large-scale automatically annotated conversations through a scalable pipeline for model training. Both corpora provide synchronized participant-level speech tracks and multi-dimensional fine-grained annotations. We further release the SmoothConv Benchmark and evaluate these resources on speech separation, multi-speaker automatic speech recognition (MSASR), and turn detection tasks. Experimental results demonstrate the utility of the proposed resources for multi-party speech interaction modeling. The datasets, benchmark, and related resources are publicly available.

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Chengyou Wang, Mingchen Shao, Chunjiang He, Zeyu Zhu, Jierui Guo, Bingshen Mu, Zikai Liu, Hanke Xie, Yuhang Dai, Zhou Zhu, Lei Xie. 2026-10-08. SmoothConv and DuplexConv: Complementary Mandarin Multi-Party Conversational Speech Corpora for Speech Interaction. https://arxiv.org/abs/2610.11150

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