TwinMind Launches Ear-3: A Voice AI Model
TwinMind, an AI startup founded by former Google X scientists, has announced the launch of Ear-3, its newest voice AI system.
The model is setting fresh benchmarks in accuracy, speaker separation, language support, and cost—making advanced speech recognition more powerful and accessible than ever before.
The release comes at a time when businesses, educators, healthcare providers, and everyday users increasingly depend on accurate voice-to-text technology.
What Is Ear-3?
Ear-3 is an automatic speech recognition (ASR) system. Like other transcription tools, it turns spoken words into text.
But what sets it apart is its ability to work accurately in noisy environments, detect who is speaking in group conversations, and handle complex language switching, also known as code-switching.
The model was trained on huge collections of real-world audio, including podcasts, interviews, and films.
This helped it learn how people actually talk—not just in perfect studio recordings but also in classrooms, busy cafes, and international conference calls.
Key Features
Works Online and Offline: Ear-3 runs in the cloud because of its size. But if the internet cuts out, the app automatically switches to the smaller Ear-2 model on your device. Once you’re back online, it goes back to Ear-3.
Strong Privacy: Audio files are not kept for long. Only the transcripts are saved, and users can choose to store them with encryption. The voice recordings are deleted soon after processing.
Easy Integration: An API for developers and companies will be available in the coming weeks. For everyday users, TwinMind will release features on iPhone, Android, and Chrome apps by next month. Pro users will also get longer transcription limits.
Background
TwinMind isn’t just about Ear-3. Earlier this year, the startup raised $6 million in funding to build what it calls a “second brain” AI that can remember context and improve productivity tools.
Ear-3 is part of that bigger mission to make advanced AI both powerful and practical.
Benchmark Perforance
Word Error Rate (WER): 5.26%
This is currently the lowest in the world. Out of 100 spoken words, fewer than six are misheard. That beats competitors like OpenAI Whisper, AssemblyAI, Deepgram, and ElevenLabs.
Speaker Diarization Error Rate (DER): 3.8%
This means the model can very reliably separate voices when multiple people are talking.
For businesses running meetings, journalists recording interviews, or customer service centers handling calls, this is a huge step forward.
Language Support: 140+ languages
Ear-3 covers a wider range than almost any other public ASR. From widely spoken languages like English, Spanish, and Mandarin to regional tongues like Hindi, Tamil, and Swahili, the model is built for global communication.
How It Works
Ear-3 uses a multi-step pipeline to boost accuracy:
Audio Cleaning – Removes background noise and distortion.
Speaker Tracking – The model precisely marks when different speakers are talking.
Language Recognition – Ear-3 can recognize mixed language inputs and adapt to region-specific pronunciation and slang.
Context Awareness – It uses semantic analysis to ensure words fit the overall meaning of the conversation, reducing misunderstandings in legal, medical, or professional transcripts.
Price
Ear-3 is very affordable at just $0.23 per hour of transcription. This makes it useful for everyone—from students and journalists to large businesses.
Potential Uses
Healthcare: Doctors can dictate notes more reliably during patient visits.
Education: Real-time captions help online learners and students with hearing impairments.
Customer Service: AI voicebots can now work in dozens of languages, serving global customers.
Media & Journalism: Reporters and podcasters save hours on manual transcription.
Finance & Security: Voice biometrics add a layer of fraud detection and identity verification.
By offering low-cost, high-accuracy speech recognition across so many languages, TwinMind is making voice AI more inclusive especially for regions like India, Africa, and Southeast Asia where many languages are spoken daily.
The Road Ahead
According to the company, Ear-3 will keep expanding with new features, more languages, and stronger developer tools.
API access will open soon, and the mobile and browser apps will start reaching users within weeks.
News Gist
TwinMind, founded by ex-Google X scientists, has launched Ear-3, a breakthrough voice AI model with the world’s lowest word error rate (5.26%), 140+ languages, top speaker recognition, and just $0.23/hour transcription, making speech AI more accurate, affordable, and global.
FAQs
Q1. What is TwinMind Ear-3?
Ear-3 is a new AI-powered speech recognition model with record-breaking accuracy, multilingual support, and low-cost transcription.
Q2. How accurate is Ear-3?
It achieves a world-best 5.26% Word Error Rate, outperforming Whisper, Deepgram, and AssemblyAI.
Q3. How many languages does Ear-3 support?
It supports over 140 languages, including major and regional ones like Hindi, Tamil, Spanish, and Arabic.
Q4. What makes Ear-3 affordable?
It costs just $0.23 per transcription hour, one of the lowest prices in the industry.
Q5. Does Ear-3 work offline?
Yes, if internet drops, it switches to the smaller Ear-2 model locally until the connection returns.
Q6. When will apps and APIs launch?
API access for developers will roll out in weeks, with consumer features coming to iPhone, Android, and Chrome apps by next month.
