Speaker-Reasoner is an end-to-end speech LLM that iteratively analyzes audio structure, predicts temporal boundaries, and jointly models speaker identity, gender, timestamps, and transcription using a speaker-aware cache for long audio.
Train short, infer long: Speech-llm enables zero-shot streamable joint asr and di- arization on long audio
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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.
JSTIP interleaves speech and text sequences during pretraining on 38k hours of ASR data to improve entity accuracy over ASR-only and simple joint-training baselines while matching performance from domain text.
LLM-based multi-talker ASR with dual-encoder, feature interleaving, length-aware speaker loss, and adaptive ASR threshold achieves 18% and 24% relative gains over baselines on AliMeeting and Aishell4.
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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