What happened this week
Article 50 of the EU AI Act came into force on 2 August. It requires two things of anyone shipping a voice agent into Europe: tell the caller they are talking to a machine, and mark synthetic audio so software can detect it later. Five weeks on, the industry has answered a different question than the one it was asked.
Residency and redaction, not disclosure
Three platforms shipped compliance-shaped features. LiveKit 1.7.0 strips 41 kinds of personal data out of call recordings, traces and logs before any of it is stored. Concretely, that means a caller can read out a card number and it never lands in your logging system. Vapi added Azure region pinning, so you can require that a call is processed in, say, Germany rather than wherever is cheapest. OpenAI opened per-request regional processing, which is the same idea at finer grain.
All three answer where data lives and what gets kept. That is a privacy question, and a real one. None of them tells a caller they are speaking to a machine, and none of the three announcements names Article 50.
Regulators were quiet too. The Commission's Article 50 FAQ still reads "Last update: 24 July 2026" and the Code of Practice page still reports about 190 signatories as of end July. EDPB, California's AG, the CPPA, C2PA, Ofcom, the FTC and the FCC published nothing on synthetic speech, and the UK's DRCF has posted nothing since its call for input opened on 25 June.
The marking half may not have held anyway. An audio watermark is meant to work like an invisible signature pressed into the waveform, one that survives compression and re-recording. A single-author paper at APSIPA shows you can find which part of the signal the signature is hiding in using cheap probes, then wipe it with one targeted edit. No training, no access to the watermarking system. If you were planning to satisfy a marking obligation by watermarking your output, this is the paper to read first.
The modular stack keeps getting built
There are two ways to build a voice agent that can be interrupted. One is a single model that hears and speaks at once, trained end to end. The other keeps the pieces separate, so speech recognition, turn-taking and synthesis are swappable parts you can debug and replace one at a time.
The separate-parts camp had a good week. X2Streaming-TTS starts speaking from a partial sentence rather than waiting for the whole thing, reaching first audio in 15.8ms. Five of its seven authors also wrote last week's X2-Turn, which handles the listening half. One company is assembling a real-time voice stack part by part without training an end-to-end duplex model at any point, and nothing rebutted that. No newly named end-to-end full-duplex dialogue model appeared for the third window running. Our count of full-duplex and turn-taking papers now reads 0, 5, 2, 3, 2 across five weeks, with nothing on barge-in, endpointing or VAD.
Hear2Act puts a number on why keeping the parts separate might be reasonable. The test: a caller says something where the words are calm but the voice is not, and the assistant has to pick the right action. Giving the model the audio alongside the transcript moves its success rate from 14.6% to 15.3%, which is almost nothing. Making it write down what it heard in the voice first, before deciding, takes the same models to 39.6%, against 40.7% if you simply hand it the right answer. The information was in the audio the whole time. It only became useful once something forced the model to say it out loud.
Two labs graded their own homework, honestly
Word error rate leaderboards are how most teams pick a speech recognition engine. Hume's paper shows leading open models reciting the expected benchmark answer even when the audio plainly says something else, which means part of that score is memorisation rather than listening. The same week, Boson AI published a model whose own card declares it ineligible for the Open ASR Leaderboard and labels its numbers "benchmark-fitted development measurements only". The practical takeaway: test a candidate engine on your own recordings before trusting its rank.
Open weights, and a consent answer
FireRedAudio puts understanding and generation on one 9B backbone under Apache-2.0, so a single model transcribes, answers, speaks and edits speech. LAION shipped a voice-acting TTS with 500 per-character voice adapters and every training set under CC-BY-4.0. All 500 voices are invented rather than cloned from anyone, which is a direct answer to the consent question Japan's Ministry of Justice raised this month: you can ship a character voice without needing a release from a real speaker. Audio8 released a 170M cloner under a new licence that charges once your revenue passes a threshold, while leaving its earlier model on Apache-2.0.
Still dark
ByteDance's SeedRealtime is four weeks old with no technical report, no weights, no paper and no API. Its page carries exactly two outbound links, both to itself. The post says the system is fully rolled out at scale and halves conversational pacing problems against cascades, with no eval protocol, no baseline and no numbers, so there is nothing anyone outside ByteDance can check. Sarvam's Saaras V4 is a month past announcement with no weights, and xAI's grok-voice-latest reroute is 19 days past its stated date with the docs still in future tense.
Money
Wispr Flow raised $280M at $2B and previewed Canto, its first in-house speech model. And the year's largest voice-plus-digital consolidation stalled: LivePerson never opened the polls on the SoundHound merger, adjourning to 2 September. The problem was turnout, not opposition. The deal needs a majority of every share that exists, not just of those voted, and over 97% of votes cast were in favour.