Note 2026-W36 · A 30-hour hand-labelled corpus of dyadic conversation plus a fixed protocol for scoring end-of-turn and interruption detection, conversation type controlled across six styles, every dialogue triple-annotated at Fleiss's kappa 0.78. Fourteen systems were scored. End-of-turn recall is stable across types; interruption false positives concentrate in backchannel-dense talk. No system is simultaneously fast, high-recall and low on false positives. Disclosure: the training split is oto data

Note 2026-W36 · Two omni-modal models converse in native audio with no external speech recognition or synthesis and no API boundary, over the unmodified tasks, tools and success checks of an established text agentic benchmark, so interaction modality is the only variable and the training loop stays local and differentiable. Existing setups either cascade TTS and ASR around a proprietary voice API, where gradients cannot flow, or stay in text and can measure voice agents without improving them. The re

Note 2026-W36 · Models how gaze, speech and perceived interpersonal closeness signal floor changes in free four-person dialogue, using the GaMMA corpus and interpretable logistic regression over behaviourally motivated features extracted before each turn-taking event, classifying outcomes as gaps or overlaps. Most turn-taking work this window is dyadic and audio-only; this is the multi-party, multimodal case, and it deliberately trades detector accuracy for features a designer can reason about.

BreezeBlue/Breeze-TTS-2

Text-to-Speech • 3B • Updated 6 days ago • 2.24k • 264

Note 2026-W36 · A text-to-speech release covering voice cloning, voice design and voice direction, which reached 215 likes and about 1,800 downloads in its first week and now leads the Hugging Face text-to-speech trending list. The licence is split and worth reading before integration: source code is Apache-2.0, but the model weights, any derivative models, and self-hosted outputs are restricted to research and non-commercial use. BreezeBlue is a new name in this digest and has been added to the org

nvidia/Nemotron-3-Diarization-preview

Voice Activity Detection • Updated 2 days ago • 31 • 27

Note 2026-W36 · A streaming Sortformer speaker-diarization and speaker-tagging preview, currently the top trending voice-activity-detection repo on Hugging Face. Access is gated by manual review under the NVIDIA Software and Model Evaluation License: internal test and evaluation only, not production, only on NVIDIA GPUs, no redistribution, no using outputs or artifacts to develop another model, and no disclosure of evaluation or test results without NVIDIA's prior written consent. That last clause ma

Note 2026-W35 · 480 persona-grounded scenarios hold the task fixed and vary whether the user's concern is stated in words or carried only in prosody, with objectively checkable outcomes. Giving the model the audio on top of the transcript moves the optimal-solution rate from 14.6% to 15.3%. Forcing it to first write the inferred concern into text takes the same models to 39.6%, against 40.7% for ground-truth state. The prosody is recoverable, and it still does not reach the action unless something ma

Note 2026-W35 · Synthesises from uncertain token prefixes instead of waiting for a sentence, using uncertainty-aware buffering and carrying decoder state across segment boundaries. Reported at 15.8ms median time to first token for a single request and 260.8ms at 128 concurrent. Five of its seven authors also wrote X2-Turn, the streaming turn-state model from last week, so one company is now assembling a real-time voice stack part by part without training an end-to-end duplex model at any point.

Note 2026-W35 · Instead of sweeping blindly through distortions, this uses cheap structural probes to locate the domain a watermark is embedded in, then applies a single attack matched to that domain, and reports a threshold-free fragility score per scheme. It needs no training and no access to the watermarking model. Anyone planning to satisfy a marking obligation with a neural audio watermark should read it before treating that watermark as the compliance artefact.

Note 2026-W35 · Adds real-time predicted backchannels and head nodding to a voice-cloned avatar and measures the effect in a within-subjects study of 35 people. Perceived attentiveness, the sense of talking with the real person, and co-presence all improve significantly. The argument is that a duplex agent feels present because of how it listens, not how well it speaks, which is a case for spending latency budget on the listening side.

Note 2026-W35 · Three behavioural probes, covering reference disagreement, masked-number recovery, and orthographic switching, show leading open ASR models reproducing verbatim benchmark reference spans even when the audio contradicts them, masks them, or leaves them ambiguous. The behaviour can be steered with a low-rank direction, which makes it a learned policy rather than an artefact. Anyone choosing a backbone off a WER leaderboard is reading a number that partly measures memorisation.

FireRedTeam/FireRedAudio

Updated 6 days ago • 34

Note 2026-W35 · One backbone with two decoupled continuous paths, an audio encoder for understanding and a RedAE path for generation, covering ASR, audio understanding, zero-shot and instruct TTS, semantic and acoustic speech editing, and temporal grounding over recordings up to an hour. Weights are up under Apache-2.0. The benchmark claims on MMAU, MMSU, Seed-TTS-Eval and InstructTTSEval are self-reported and the linked paper is still a placeholder, so treat the scores as unverified.