Record your voice. Earn USDC. Power the world’s ears for AI. $SLC unlocks higher earning tiers. Join +2m Silencian's in over +180 countries 🌎
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Level-up your Earnings with $SLC
In coordination with the @FoundationSLC, we are unlocking enhanced $SLC utility starting today.
SLC holders can now earn up to 100% more through Silencio Voice AI.
Live and available to all Silencio Voice AI users as of now.
What is it?
🔹Hold more $SLC → Move up levels
🔹Higher level → Higher % boost on your rewards
🔹Up to 100% boost at the highest level
Why this matters:
🔹Direct earning power tied to $SLC holdings
🔹Stronger flywheel between enterprise revenue, token burns, and contributor rewards
🔹A clear economic reason to hold $SLC on your wallet
Much more is coming to continue driving $SLC utility and placing the token at the absolute core of the Silencio ecosystem.
Every new dataset, every enterprise contract, every incentive layer will increasingly reinforce SLC as the coordination engine of the network.
We are building the world’s ears for AI and robotics.
Visit Voice AI today and start earning: ai.silencio.store/opportunit…
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As the MiCA transitional period comes to an end, many projects are now focusing on regulatory compliance.
Together with the @FoundationSLC, transparency and regulatory clarity have been a priority from the beginning.
Our MiCA crypto-asset whitepaper has already been published and is available: whitepaper.silencio.network
We're focused on building the World's Ears for AI & Robotics.
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Why do voice AI systems trained on 100 voices fail in the real world, while ones trained on millions of voices work everywhere?
Because a voice carries dozens of things the AI has to learn to ignore. Pitch. Tone. Accent. How fast you talk. Where in the mouth you make a sound. Whether you're excited, tired, congested. Whether you're 8 or 80. Whether you grew up speaking the language or learned it later.
If the AI only ever hears one slice of that, say 30-year-old American men reading in a studio, it learns "this is what speech sounds like" and then breaks on anyone outside that slice. This is not theoretical. It's exactly why older voice assistants were notoriously bad at women's voices, children's voices, regional accents, and elderly speakers.
The fix is boring but it works. The training has to look like the people who'll actually use the AI. 2 million contributors across 180+ countries gets you closer to that than anything else out there.
Every contributor adds one more direction the AI can no longer be fragile in.
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Many of you switch between languages mid-sentence without even thinking about it. "I went to the market and bought iyán." "Vou para casa, parking is full." Totally natural way to talk. And one of the hardest things for voice AI to handle. 1/3
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Most voice AI is trained on one language at a time. It has to pick which language it's hearing before it can do anything. When you switch mid-sentence, it either freezes, refuses, or makes up words. Hindi-English, Tagalog-English, Swahili-English, Spanglish, Arabic-French. None of these work well today. 2/3
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Fixing this needs recordings of people actually mixing languages mid-sentence, with the switch points marked. That kind of recording basically does not exist at scale. If you mix languages in your normal speech, recording yourself just talking the way you actually talk is incredibly valuable. 3/3
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A language is not just a way of communicating. It carries a way of thinking, a way of joking, a way of arguing, a way of grieving. There are concepts in some languages that have no clean equivalent in any other.
If a language disappears, those concepts don't move to another language. They are simply gone. 1/2
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Roughly 40% of the world's languages are considered endangered. Many won't have a fluent speaker by the end of this century without intervention.
Recording them, in real conditions and with proper metadata, is one of the most direct things technology can do for them.
We're trying not to lose any of them. 2/2
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"OTS", short for off-the-shelf, is a term you'll see across our work. Worth defining, because it's a useful lens on the whole industry. 1/3
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When an AI team needs voice data, they have two options.
Option A: commission a new recording project from scratch. Hire speakers. Set up sessions. Wait months. Pay a lot.
Option B: buy existing data from a provider who already has it. Off-the-shelf. Cheaper, faster, broader coverage. 2/3
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Every recording you make is way more useful if it comes with the right details attached. The language you spoke in. The country you grew up in. The kind of room you were in. Boring sounding. Actually the difference between data we can use and data we can't. 1/3
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When you record on the Silencio Voice AI Platform, those details get saved with the audio. The language, your country, your regional way of speaking if you set it, the device, the environment, your age range, your gender. Audio on its own is just sound. Audio with these details is something a voice AI can actually learn from. 2/3
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If the language you set is Swahili but the recording is mostly in English, the AI that trains on it gets confused. So does every other recording grouped with yours. Twenty seconds of attention from you means weeks of cleaner training for the AI down the line. The single most useful thing you can do every recording. 3/3
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Marketing Communication
Silencio has published its B1 Token Transparency Filing.
Thank you to @Blockworks for reviewing the filing and providing an overview of the disclosed information below.
---
The crypto-asset whitepaper has been published and is available at: whitepaper.silencio.network
Website: silencio.network
Contact: info@silencio.network
This crypto-asset marketing communication has not been reviewed or approved by any competent authority in the European Union. The offeror or the entity seeking admission to trading of the crypto-asset is solely responsible for the content of this crypto-asset marketing communication.
The voice data to train the next generation of AI models doesn’t exist yet.
Silencio is building it across languages, dialects, and professions the labs need.
Their B1 Token Transparency Filing received only 1.5 gaps.
Token live since January 24, 2025.
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A common instinct when someone hands you a recorder: speak more clearly, more slowly, more carefully than you normally would. Project. Enunciate. Sound like a news anchor.
Please don't.
A speech AI system has to work in the field. For a farmer asking for the weather, for a teenager messaging a friend, for someone speaking on a noisy bus. None of those people sound like a news anchor. If the model only learns "news anchor" speech, it fails on everyone else. This is a documented failure mode of older voice products. They trained on too-clean data and broke the moment they met a real user.
The Silencio catalog is valuable precisely because it captures how people actually talk. Pauses, "ums", regional cadence, conversational pace, words you'd never bother to articulate carefully because you don't need to.
The technology has to learn that. We need you to give it to us.
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The term "low-resource language" gets used a lot in AI. Worth unpacking, because most of the languages in this category have plenty of speakers. They have low DATA resources, not low human ones. 1/3
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A language is called "low-resource" when there are fewer than ~1,000 hours of publicly available recorded audio in it.
Swahili has 200+ million speakers. By that definition, until recently, it was low-resource.
Yoruba has 40+ million. Low-resource.
Tagalog, Hausa, Amharic, Igbo, Oromo, Sindhi, Tigrinya. All of them, low-resource. 2/3
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What that means in practice: voice assistants don't work, transcription doesn't work, captioning doesn't work, voice search doesn't work. Hundreds of millions of native speakers, technologically excluded from products built for English and Mandarin.
Closing that gap is the work. Recording by recording. 3/3
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New website. Same mission.
Building the World's Ears for AI & Robotics
Take a look at the new home of Silencio 👇
🌍 Built by the community.
📊 Powered by real-world data.
🔊 Driven by Silencio.
silencio.network
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Why does a quiet recording matter? Not for aesthetics. For math.
A speech model learns by separating "the signal" (your voice) from "everything else" (background noise). If the noise overwhelms the signal, the model can't isolate the words. If the noise is unusual, like a TV at full volume, music, or an air-conditioning unit, the model learns to associate your language with that noise too. Then, in the real world, it fails on anyone whose room is quiet.
Same logic for articulation. If you over-pronounce, going slow, careful, projecting, the model learns "this is what this language sounds like" and then can't handle a normal conversational pace.
The single most useful rule: record the way you actually speak, in a room that sounds the way your normal rooms sound. The model needs to learn your real voice, not a performance of it.
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