Dixtral uses diarization conditioning on a Whisper-based encoder within Voxtral to outperform baselines on multi-speaker transcription and match or exceed on QA tasks.
MOSS transcribe diarize: Accurate transcription with speaker diarization,
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
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.
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.
MOSS-Audio is an audio-language model using a 12.5 Hz encoder, DeepStack cross-layer injection, time markers, and an event-preserving annotation pipeline for unified audio understanding.
citing papers explorer
-
Grounding Spoken LLMs in Multi-Speaker Audio via Diarization Conditioning
Dixtral uses diarization conditioning on a Whisper-based encoder within Voxtral to outperform baselines on multi-speaker transcription and match or exceed on QA tasks.
-
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.
-
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.
-
MOSS-Audio Technical Report
MOSS-Audio is an audio-language model using a 12.5 Hz encoder, DeepStack cross-layer injection, time markers, and an event-preserving annotation pipeline for unified audio understanding.