What happened this week
Evaluation took the front seat. A vendor-scale human study and an academic audit land on the same conclusion from opposite directions: the field still lacks a trustworthy way to score voice AI, and the shortcuts in current practice are now measurable.
Evaluation — humans and judges disagree
RW-Voice-EQ Bench (Ayllon, Baird, Brooks et al.) is the paper behind Hume's Real World VoiceEQ release: 40+ systems, 15+ dimensions, over a million human ratings across ASR, TTS, S2S, and speech understanding. No system ranked top-5 across all eight capability groups, and for S2S, access to audio does not guarantee use of it — some agents remain largely transcript-driven. The complement is Auditing Protocol-Level Shortcuts in Large Audio Language Model Judges (Park, Chan, Saito et al.): corrupting the specialist label collapses emotion-judgement accuracy to 0.10 or below in five of six LALM judges. If your eval stack is an LALM judge, this is a failure mode you now have to rule out.
Foundational — Inkling, SALMONN-2, and a privacy result
Inkling is Thinking Machines Lab's first open-weights release: a 975B-total / 41B-active MoE, Apache-2.0, 1M-token context, with audio handled encoder-free — 100 ms chunks of discretised mel spectrogram fed straight into the transformer. A reported 91.4% on VoiceBench puts it at the top of open-weights speech understanding. Audio input only, no speech output, but day-one vLLM / SGLang / llama.cpp support makes it an obvious new base for audio-understanding stacks. SALMONN-2 (Yang, Xu, Yu et al.) argues the encoder side differently: a unified self-supervised encoder with multi-layer feature fusion matches specialised supervised encoders with more balanced coverage. And Do Speech Tokens Leak Voiceprints? (Lu, Yan, Zhang et al.) shows three seconds of frontend token output from Moshi, Higgs3, Kimi-Audio, or Qwen3-Omni suffices to recover a speaker embedding at cosine similarity above 0.70 — a concrete privacy attack on the exact token interfaces production S2S models expose.
Platform layer
ElevenLabs' July 13 changelog ships backchannel detection — filtering "uh-huh"-type listener utterances so they do not trigger turns — plus nested agent transfers and a run_subagent delegation tool. Backchannel filtering is a small line item that is squarely a duplex problem, landing in the most widely deployed agent stack. AssemblyAI's Sync API returns a finished transcript in one HTTP call at a claimed ~134 ms p50, $0.45/hr — for turn-level transcription where a streaming session is overkill.
Dataset
Dialogs (Shigabeev, Latyshev) is a 20.6-hour studio-quality Russian corpus of acted face-to-face dialogs with per-utterance style and emotion labels — read-speech resources rarely capture this turn-taking rhythm.
Also shipped, briefly
Sber's GigaAM-Multilingual ASR foundation family (MIT, 70+ languages, strong Central Asian coverage); livekit-agents 1.6.6 with runtime STT/VAD/LLM/TTS hot-swap; Deepgram's Flux numerals and refreshed Nova-3 monolingual models; Resemble made watermarking the default ahead of the EU AI Act's Aug 2 transparency deadline; and audio.cpp 0.3 broadened its GGUF speech runtime.
What is not here
W29 (Jul 6–12) was skipped by the scheduled task; OpenAI's GPT-Live-1 (Jul 8) and Cartesia Ink-2 (Jul 9) fall in that gap and remain backfill candidates rather than W30 items. Pipecat 1.6.0 and Trelis tiron landed Jul 21, next week's window. Rime's $24M Series A (Jul 15) is in-window but funding, not a ship.
Corrections to hello@fullduplex.ai.