INTERNAL TESTING TRACK — ACTIVE VIA GOOGLE PLAY

YourVoices

Restoring digital privacy — one voice sample at a time.

Download Android test app Explore the manifesto

Android APK for invited voice-sample testing. Install only if you trust this test track.

The Dead Internet & Our Response

The phenomenon of the "Dead Internet" has become a tangible reality affecting most users of the global digital media space today. A state of doubt and uncertainty has taken hold: is the entity I am communicating with — the one sending me information — a real human being or a fabricated persona?

It was from this standpoint that Monopeak took this dilemma seriously and worked tirelessly to devise private technological solutions that restore a portion of the trust users have lost in the digital world. And from here, our latest project was born: YourVoices.

⚖️

Platform Rules: The Private Privacy Charter

Our platform contains no convoluted privacy agreements or twisted terms — because we simply do not own nor collect any information about anyone, unless they personally and of their own free will choose to publish it and confirm their possession of it through the unique voice sample verification.

The vast majority of humanity has come to see the entire internet as a grand surveillance apparatus and a tool for violating digital sanctities. The prevailing perception among individuals is that "our privacy is the cheapest commodity in the global technology market."

And here, the voice arrived — the voice of every human being who suffers from the violation of their digital rights and refuses to be merely a number in an algorithm. Monopeak's philosophy is built on breaking the mould of blind compliance: you should not stand submissively just because everyone else decided to stand when the red light flashes, and you should not accept — neither programmatically nor humanly — something you are not convinced of deep within. YourVoices is the space that restores that trust and complete privacy to you.

Core Capabilities

🎙️

Voice Sample

Authenticate using your unique voice sample — no passwords, no recovery emails, no third-party identity providers. Learn about the science →
🛡️

Private Identity

Every user holds a self-owned identifier (V-ID). Your identity belongs to you, stored on your device, not in a central database.

✉️

Private Messaging

End-to-end encrypted whispers reach only the intended recipient. No metadata trails, no server-side message inspection.

📞

Voice Calls

Peer-to-peer encrypted voice calls. Your conversations stay between you and the person you trust.

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🎙️ Voice Sample

How voice sample verification works — experimental results & technical references

What Is a Voice Sample?

A voice sample is a 512-dimensional mathematical vector (embedding) extracted from your voice. Unlike a password that you must remember or a fingerprint that requires special hardware, your voice sample is computed from a short audio recording (7 seconds) and serves as a soft biometric identifier — not a hard cryptographic proof, but a statistically reliable signal that helps confirm your identity.

The term "voice sample" is deliberately chosen over "voice print" to avoid the misleading implication that voice is as unique and immutable as a fingerprint. Voice is behavioural: it changes with health, emotion, age, and recording conditions. It is a sample taken at a moment in time, compared against a stored centroid (the average of several enrolment samples).

The Pipeline

From raw audio to similarity score — every step runs entirely on-device:

# 1. Audio capture (Android AudioRecord) 16 kHz mono 16-bit PCM ── 7 seconds # 2. DSP — Rust fbank_cli (identical pipeline on device & test harness) Pre-emphasis (α=0.97) → Framing (25ms, 10ms stride) → Hamming window → FFT (512-point) → Mel filterbank (80 filters, 20-8000 Hz) → Log(|FFT|²) ── 80-dimensional FBank × T frames # 3. CMVN (Cepstral Mean Variance Normalization) For each mel bin: x ← (x - μ) / max(σ, 1e-10) → Zero-mean, unit-variance per frequency bin across time # 4. ONNX Inference — CAM++ model (voxceleb_CAM++.onnx, 29 MB) Frozen feature extractor (no fine-tuning), graph optimizations disabled → 512-dimensional embedding vector # 5. L2 Normalization + Cosine Similarity emb ← emb / ||emb||₂ → score = cosine(probe_emb, stored_centroid)

Experimental Results

The system was evaluated on VoxCeleb1 (1,211 speakers, ~148k utterances) using the production-identical pipeline: Rust DSP → CMVN → CAM++ ONNX (ORT_DISABLE_ALL).

0.289
Mean Similarity Gap
(target ≥ 0.25 ✅)
~9.2%
Equal Error Rate
(target ≤ 5% ❌)
0.70
Operating Threshold
(conservative, security-tilted)

Key insight: The EER of ~9% reflects a frozen CAM++ model used as a feature extractor with cosine similarity — no fine-tuning, no PLDA backend calibration. Published state-of-the-art results (1-2% EER) are achieved after full training on VoxCeleb with data augmentation and backend calibration. This ~9% figure is the honest, measured upper bound of our current pipeline on noisy, real-world data.

Two-speaker validation: In a controlled test with two distinct speakers (clean recordings, known ground truth), the system achieved a mean gap of 0.342 with zero overlap between intra-speaker and inter-speaker similarity distributions — comfortably above the 0.30 red line.

Component Verification Status

Component Verification Method
DSP (Rust fbank_cli) ✅ Bit-exact with device pipeline (flat FBank match)
CMVN (axis=0, ε=1e-10) ✅ Zero-mean verified per bin; matches Kotlin implementation exactly
ONNX (ORT_DISABLE_ALL) ✅ maxAbsDiff < 1e-4 between device and Python reference
L2 normalization ✅ Every embedding has ‖x‖ = 1.0 ± 1e-6
VoxCeleb1 (dev, ~9% EER) ⚠️ Estimated on 30 speakers × 5 utterances; official vox1_test evaluation pending file availability

Reproduce the Results

The full test harness is in the repository under cmvn_experiment/voxceleb/:

# 1. Install dependencies pip install numpy soundfile onnxruntime # 2. Extract FBank with the Rust CLI (production-equivalent DSP) cargo build --release --bin fbank_cli ./target/release/fbank_cli input.wav features.json # 3. Run the analysis pipeline python3 cmvn_experiment/voxceleb/run_voxceleb_test.py
  1. fbank_cli — Rust FBank extractor, identical to the Android app's native DSP
  2. CMVN — Per-bin mean subtraction and variance normalization (Python, matched to Kotlin)
  3. ONNX inference — voxceleb_CAM++.onnx, ORT_DISABLE_ALL to match device output
  4. Cosine similarity — All-pairs intra-speaker vs inter-speaker scoring → EER via threshold sweep

References & Further Reading

  • 🔗 voice-vector
    Python package for voice embedding extraction using CAM++ and ECAPA-TDNN models — useful for cross-validation and experimenting with different back-ends
  • 📄 CAM++: A Fast and Efficient Neural Network for Speaker Verification (2023)
    The paper describing the CAM++ architecture used in our ONNX model. Achieves 0.85% EER on VoxCeleb1 with full training and PLDA backend
  • 📊 VoxCeleb1 Dataset
    1,211 celebrities, ~148k utterances, extracted from YouTube videos. The standard benchmark for speaker verification research
  • 🔧 VoxCeleb Trainer
    Official training and evaluation code for VoxCeleb — the reference implementation for metric learning and PLDA back-end calibration
  • ⚡ ONNX Runtime
    Cross-platform inference engine used on both Android (Kotlin) and the test harness (Python). The ORT_DISABLE_ALL flag is critical for numerical equivalence
  • 🎵 Librosa
    Python audio analysis library. Not used in our pipeline — we use Rust fbank_cli for DSP equivalence — but useful for understanding FBank theory and visualisation
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