DM-ASR reformulates multi-speaker ASR as multi-turn dialogue generation conditioned on diarization results, achieving competitive benchmark performance with relatively small models and limited data.
Diarizationlm: Speaker di- arization post-processing with large language models,
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
SoulX-Transcriber is a unified LLM framework for end-to-end multi-speaker transcription using two-stage training (speaker-aware pre-training then supervised fine-tuning) that reports strong results on AliMeeting, AISHELL-4, and AMI.
LLMs detect social signals in clinical transcripts across model families, with an agreement-weighted ensemble using group-level agreement patterns improving accuracy and stability over individual models.
citing papers explorer
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DM-ASR: Diarization-aware Multi-speaker ASR with Large Language Models
DM-ASR reformulates multi-speaker ASR as multi-turn dialogue generation conditioned on diarization results, achieving competitive benchmark performance with relatively small models and limited data.
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SoulX-Transcriber: A Robust End-to-End Framework for Multi-Speaker Speech Transcription
SoulX-Transcriber is a unified LLM framework for end-to-end multi-speaker transcription using two-stage training (speaker-aware pre-training then supervised fine-tuning) that reports strong results on AliMeeting, AISHELL-4, and AMI.
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SocialLM: Social Signal Processing of Patient-Provider Communication using LLMs and Contextual Aggregation
LLMs detect social signals in clinical transcripts across model families, with an agreement-weighted ensemble using group-level agreement patterns improving accuracy and stability over individual models.