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.
Speakerlm: End-to-end versa- tile speaker diarization and recognition with multimodal large lan- guage models
7 Pith papers cite this work. Polarity classification is still indexing.
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MSU-Bench is a new two-tier benchmark covering speaker grounding to dialogue reasoning in multi-speaker conversations, with Gemini-assisted annotation and human verification.
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.
Introduces TPI-Train dataset and TPI-Bench to mitigate semantic shortcut learning in SLMs by enforcing acoustic cue prioritization for third-party interruption handling.
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.
PASK introduces the DD-MM-PAS paradigm for streaming proactive agents with intent-aware detection, hybrid memory modeling, and a new real-world benchmark where the IntentFlow model matches top LLMs on latency while finding deeper intents.
citing papers explorer
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Speaker-Reasoner: Scaling Interaction Turns and Reasoning Patterns for Timestamped Speaker-Attributed ASR
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.
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MSU-Bench: Towards Speaker-Centric Understanding in Conversational Multi-Speaker Scenarios
MSU-Bench is a new two-tier benchmark covering speaker grounding to dialogue reasoning in multi-speaker conversations, with Gemini-assisted annotation and human verification.
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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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Still Between Us? Evaluating and Improving Voice Assistant Robustness to Third-Party Interruptions
Introduces TPI-Train dataset and TPI-Bench to mitigate semantic shortcut learning in SLMs by enforcing acoustic cue prioritization for third-party interruption handling.
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Balancing ASR and diarization in end-to-end LLMs for multi-talker speech recognition
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.
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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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PASK: Toward Intent-Aware Proactive Agents with Long-Term Memory
PASK introduces the DD-MM-PAS paradigm for streaming proactive agents with intent-aware detection, hybrid memory modeling, and a new real-world benchmark where the IntentFlow model matches top LLMs on latency while finding deeper intents.