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# Speaker Diarization

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  1. arXiv

    Meeting transcription stops reading only who is talking: structured speaker conditioning steadies target recognition under overlap

    Meeting transcription must output text for a requested speaker, but four-state activity conditioning collapses different interferer counts and hand-over histories into one target condition. Structured speaker conditioning encodes each speaker's activity track separately, combines it with session-level acoustic identity, and controls the acoustic encoder through condition-dependent low-rank cross-channel modulation. On Qwen3-ASR-0.6B with oracle diarization, the complete method uses 1.46M conditioning parameters and achieves a 6.23% mean relative cpER reduction over four-state diagonal conditioning across four meeting test sets.

    Structured Speaker ConditioningTarget-speaker ASRConditional Low-Rank ModulationSpeaker DiarizationQwen3
  2. arXiv

    Encoder-free speech LLM pretrained on speaker-identity and emotion metadata beats a randomly initialized encoder baseline under matched data

    The work proposes metadata-supervised pretraining (MSP), which uses speech attributes such as speaker identity and emotion, together with speaker-aware utterance composition (SAUC) and random span masking, to pretrain an encoder-free speech LLM; on joint ASR and speaker diarization in multi-speaker conversations, the encoder-free model outperforms its randomly initialized encoder-based counterpart under matched training-data conditions, and with limited metadata-annotated data it is competitive with models using speech encoders pretrained on substantially larger corpora, outperforming them in several settings.

    Microsoft (United States)University of California, Los Angeles
    Mohan Shi（First author）
    Metadata-Supervised PretrainingSpeaker-Aware Utterance CompositionSpeaker DiarizationEncoder-Free Speech-LLM
  3. arXiv

    Cross-meeting speaker attribution: ThyVoice leads all evaluated commercial cascades at a 47.13 mean SI-cpWER, while per-meeting leader ElevenLabs gains about 30 percentage points of attribution error on CHiME-6

    The work introduces SI-cpWER, a metric that scores cpWER under one corpus-global speaker-ID map, and evaluates five commercial diarize-then-identify cascades, two open baselines, and the end-to-end reference system ThyVoice on CHiME-8 NOTSOFAR (clean and noise-augmented) plus CHiME-6, finding that requiring one persistent identity across meetings changes the commercial ranking: ThyVoice records lower SI-cpWER than every evaluated commercial cascade in all three conditions, with a full-panel mean of 47.13 versus 54.75 for the next system.

    Shantanu Vispute（First author）
    Persistent Speaker AttributionSpeaker Identified cpWERThyVoice Reference SystemSpeaker Diarization
  4. NVIDIA Technical Blog

    NVIDIA fine-tunes Nemotron 3.5 ASR on SADA and FLEURS, cutting Saudi Najdi and Hijazi WER from 55.05% to 29.96% while English also improves slightly

    This NVIDIA tutorial adapts a Cache-Aware FastConformer-RNNT multilingual streaming ASR model to Najdi and Hijazi Saudi dialects using minimal curation of SADA 2022 (retaining 103,559 of 125,490 utterances, 133.7 hours, 82.5%), a weighted replay mix of 90% Saudi speech with 7% English and 3% Arabic FLEURS, and duration bucketing, training for 12,000 steps in about 4.5 hours on two GPUs, which lowers Najdi+Hijazi test WER from 55.05% to 29.96% and CER from 31.63% to 12.18%, full SADA WER from 58.84% to 35.61%, while FLEURS English WER falls from 11.04% to 10.42% and Arabic WER from 12.67% to 11.41%; it also compares encoder unfreezing depths (top 6 at 33.42%, top 8 at 32.32%, all 24 layers at 29.96%) and inference-time settings ([56,13] attention context gains 1.

    NVIDIA
    NemotronFine-tuningSpeech RecognitionCatastrophic ForgettingSpeaker Diarization

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