DuplexConv DuplexConv is a large scale Chinese multi channel conversational speech dataset with LLM assisted annotations , developed by ASLP@NPU and QualiaLabs as part of the SmoothConv–DuplexConv corpus family. Companion dataset: SmoothConv on HuggingFace (100 hours, expert human annotation). DuplexConv and SmoothConv share the same conversational domains and a unified data design. SmoothConv focuses on high quality human annotations for benchmarking and supervised learning; DuplexConv emphasizes scale for Speech LLM pre training and data driven modeling. Dataset Overview DuplexConv comprises 2,000 hours of naturally occurring multi party Chinese conversations recorded in multi channel environments across Tutoring and Social Chat scenarios. The dataset captures realistic full duplex conversational behaviors, including overlapping speech, backchannels, interruptions, pauses, and dynamic turn transitions. An LLM assisted annotation pipeline generates transcripts, speaker aware conversational structures, turn level interaction information, and scene level contextual labels. Together with SmoothConv, DuplexConv bridges fine grained human annotation and large scale Speech LLM training…
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