| --- |
| model-index: |
| - name: BrightoSV-SpeakerVerification-V1.2 |
| results: |
| - task: |
| type: speaker-verification |
| name: Speaker Verification |
| metrics: |
| - type: EER |
| value: 1.184 |
| name: Equal Error Rate (%) - Bank-Grade 4s / 5-Enroll |
| - type: EER |
| value: 1.751 |
| name: Equal Error Rate (%) - Consumer 2s / 5-Enroll |
| - type: other |
| value: 3.18 |
| name: FRR @ FAR=0.1% (%) - Bank-Grade 4s / 5-Enroll |
| - type: other |
| value: 6.65 |
| name: FRR @ FAR=0.01% (%) - Bank-Grade 4s / 5-Enroll |
| - type: other |
| value: 5.3463 |
| name: Tail Gap 5% - Bank-Grade 4s / 5-Enroll |
| tags: |
| - audio |
| - speaker-verification |
| - voice-biometrics |
| - bank-grade |
| - biometric-security |
| - pytorch |
| - multilingual |
| - language-agnostic |
| - commercial-sota |
| - as-norm |
| language: |
| - vi |
| - en |
| - zh |
| - ko |
| - de |
| - fr |
| - ja |
| - ar |
| - nl |
| - id |
| license: other |
| --- |
| |
| # 🏆 BrightoSV Speaker Verification V1.2 |
|
|
| <div align="center"> |
|
|
| ### **COMMERCIAL SOTA • GLOBAL RELEASE** |
|
|
| **Bank-Grade Voice Identity Verification** |
| *(Xác thực Định danh Giọng nói cấp Ngân hàng)* |
|
|
| <div align="center"> |
| <p> |
| <img src="https://huggingface.co/datasets/thusinh1969/musan/resolve/main/BrighTO-SV-V1.2-E7-VN.jpg?download=true" width="100%" alt="BrightoSV V1.5 SOTA Performance"/> |
| </p> |
| </div> |
|
|
| </div> |
|
|
| --- |
|
|
| **BrightoSV Speaker Verification V1.2** is a **Commercial SOTA** voiceprint verification system, engineered for **offline, on-premise deployment** in banking, government, healthcare, and enterprise sectors. It answers the fundamental question: **"Is this person who they claim to be?"** |
|
|
| *(BrightoSV Speaker Verification V1.2 là hệ thống xác thực giọng nói SOTA thương mại, được thiết kế cho **triển khai nội bộ, hoàn toàn offline** trong ngân hàng, chính phủ, y tế và doanh nghiệp.)* |
|
|
| Trained on a **massive multilingual corpus with 22,000++ speakers** spanning **9+ languages** with **600,000+ speaker cohort** for score normalization, evaluated on **20 million scored pairs** — delivering statistically ironclad results at extreme operating points. |
|
|
| --- |
|
|
| ## 🏆 Key Performance Indicators |
|
|
| ### 🏦 Bank-Grade Benchmarks (4-Second QA Gate, 4s/2s Windows) |
|
|
| Evaluated on **10,000,000 positive + 10,000,000 negative pairs** with strict bank-grade QA (Audio ≥ 4s, SNR ≥ 10dB, Speech Ratio ≥ 15%): |
|
|
| | Metric *(Chỉ số)* | 3-Enrollment | 5-Enrollment | Significance *(Ý nghĩa)* | |
| | --- | --- | --- | --- | |
| | **EER** | **1.478%** | **1.184%** 👑 | 🏆 **Commercial SOTA.** Sub-1.2% on diverse multilingual test set. *(Tỷ lệ lỗi cân bằng dưới 1.2%).* | |
| | **FRR @ FAR=0.1%** | **4.31%** | **3.18%** 👑 | ✅ **Bank-Grade Achieved.** Under 5% at 1-in-1,000 impostor protection. *(Đạt chuẩn ngân hàng tại FAR 0.1%).* | |
| | **FRR @ FAR=0.01%** | **8.60%** | **6.65%** | 🔒 **Maximum Security.** 1-in-10,000 impostor protection for high-value transactions. *(Bảo vệ 1/10,000 cho giao dịch giá trị cao).* | |
| | **Tail Gap 5%** | **+4.4628** | **+5.3463** 👑 | 🛡️ **Full Separation.** 95th percentile genuine scores fully above worst impostor region. *(Phân tách hoàn toàn vùng đuôi).* | |
| | **Latency** ⚡ | — | **< 60ms** | Real-time processing on consumer GPU. *(Xử lý thời gian thực).* | |
|
|
| ### 📱 Extended Coverage Benchmarks (2-Second QA Gate, 2s/1s Windows) |
|
|
| For scenarios requiring shorter audio — mobile apps, call centers, IoT: |
|
|
| | Metric *(Chỉ số)* | 3-Enrollment | 5-Enrollment | Significance *(Ý nghĩa)* | |
| | --- | --- | --- | --- | |
| | **EER** | **2.362%** | **1.751%** 👑 | 🏆 **Best-in-Class.** Strong accuracy even with 2-second audio. *(Độ chính xác cao ngay cả với audio 2 giây).* | |
| | **FRR @ FAR=0.1%** | **8.02%** | **6.16%** | ✅ **Practical Consumer UX.** Manageable retry rate for mobile. *(Tỷ lệ xác thực lại phù hợp ứng dụng di động).* | |
| | **FRR @ FAR=0.01%** | **15.91%** | **12.58%** | 🔒 **High Security on Short Audio.** Viable for multi-factor deployments. *(Bảo mật cao trên audio ngắn).* | |
| | **Tail Gap 5%** | **+2.3151** | **+3.1082** | 🛡️ **Positive Separation.** Genuine and impostor tails clearly separated. *(Vùng đuôi tách biệt rõ ràng).* | |
|
|
| --- |
|
|
| ## 📊 Evaluation Methodology — Statistical Rigor |
|
|
| Unlike many speaker verification benchmarks that report on small test sets, BrightoSV V1.2 is evaluated at **industrial scale**: |
|
|
| | Aspect | Detail | |
| | --- | --- | |
| | **Positive pairs** | **10,000,000** (same-speaker, cross-utterance) | |
| | **Negative pairs** | **10,000,000** (different-speaker, balanced 1:1) | |
| | **Total scored pairs** | **20,000,000** | |
| | **Unique speakers** | **3,900+** in evaluation set | |
| | **Multi-enrollment** | 3 and 5 enrollment utterances, mean-aggregated | |
| | **Score normalization** | **AS-NORM** with **600,000+ speaker cohort** | |
| | **Quality fusion** | **QMF** (Quality Metric Fusion) — compensates for speaker-specific and duration offsets | |
| | **QA gates** | 5-gate bank-grade: Duration, Clipping, RMS Energy, Speech Ratio, SNR | |
|
|
| This scale ensures that operating points at **FAR=0.01%** (1 in 10,000) are backed by actual counts of **1,000 impostor threshold crossings**, not statistical extrapolation. |
|
|
| *(Quy mô này đảm bảo các chỉ số tại FAR=0.01% được xác thực bởi 1,000 mẫu vượt ngưỡng thực tế, không phải ngoại suy thống kê.)* |
|
|
| --- |
|
|
| ## 🌍 Multilingual Training — Global Voice Coverage |
|
|
| BrightoSV V1.2 is trained on a **large-scale multilingual corpus** ensuring language-agnostic voiceprint extraction: |
|
|
| | Language | Coverage | Notes | |
| | --- | --- | --- | |
| | 🇻🇳 **Vietnamese** | ★★★★★ | **Primary language.** Extensive dialect coverage (Northern, Central, Southern). *(Ngôn ngữ chính, bao phủ đầy đủ phương ngữ Bắc, Trung, Nam).* | |
| | 🇬🇧 **English** | ★★★★★ | **Primary languages** Multiple accents (US, UK, AU, Indian, Singapore) | |
| | 🇨🇳 **Chinese** | ★★★★★ | **Primary languages** Mandarin and regional variants | |
| | 🇰🇷 **Spanish** | ★★★★☆ | Native speaker corpus | |
| | 🇩🇪 **German** | ★★★★☆ | European language coverage | |
| | 🇫🇷 **French** | ★★★★☆ | Including African French variants | |
| | 🇳🇱 **Dutch** | ★★★★☆ | European language coverage | |
| | 🇯🇵 **Japanese** | ★★★★☆ | Native speaker corpus | |
| | 🇰🇷 **Korean** | ★★★★☆ | Native speaker corpus | |
| | 🇸🇦 **Arabic** | ★★★★☆ | Multiple dialect coverage | |
|
|
| **Key principle:** Speaker identity is carried by vocal tract shape, pitch dynamics, and articulatory patterns — these are **language-independent**. A speaker can enroll in Vietnamese and verify in English. The model extracts *who is speaking*, not *what is being said*. |
|
|
| *(Nguyên tắc then chốt: Định danh người nói mang tính phổ quát, không phụ thuộc ngôn ngữ. Người dùng có thể đăng ký bằng tiếng Việt và xác thực bằng tiếng Anh.)* |
|
|
| --- |
|
|
| ## 🛡️ Robustness — Augmentation & Real-World Resilience |
|
|
| The model is battle-tested against real-world audio degradation through **comprehensive augmentation** during training: |
|
|
| ### 🔊 Noise Resilience |
|
|
| | Category | Examples | Goal | |
| | --- | --- | --- | |
| | 🏙️ **Urban** | Street noise, sirens, traffic, construction | On-the-go verification | |
| | 🏠 **Domestic** | TV/radio background, appliances, children | Work-from-home reliability | |
| | 🗣️ **Babble** | Crowd noise, overlapping speakers, cafeteria | The hardest scenario — solved | |
| | ⛈️ **Natural** | Wind, rain, thunder | Outdoor stability | |
| | 🐾 **Biological** | Coughing, sneezing, baby crying | Disentangle speaker from artifacts | |
|
|
| ### 📡 Channel & Codec Resilience |
|
|
| | Codec / Channel | Simulation | |
| | --- | --- | |
| | **GSM / AMR** | Mobile telephony compression | |
| | **VoIP (Zalo, WhatsApp)** | Internet calling artifacts | |
| | **MP3 / AAC / OGG** | Lossy compression at various bitrates | |
| | **Microphone variance** | Laptop, phone, headset, far-field | |
| | **Room acoustics** | Reverb, echo, room impulse response | |
|
|
| ### 🎛️ SpecAugment |
|
|
| Time and frequency masking applied during training to prevent overfitting to specific spectral patterns, forcing the model to learn robust speaker representations from partial information. |
|
|
| **Result:** The model maintains accuracy down to **10 dB SNR** — equivalent to speaking in a moderately noisy café. Below 10 dB, the QA gate rejects the audio before inference, protecting against unreliable decisions. |
|
|
| --- |
|
|
| ## 🎯 Production Deployment |
|
|
| ### Three Security Levels |
|
|
| | Level | Min Audio | Enrollment | FAR Options | Use Case | |
| | --- | --- | --- | --- | --- | |
| | 🏦 **`bank_strict`** | ≥ 4.0s | **5 samples** | 0.1% / 0.01% | High-value banking, government *(Ngân hàng giá trị cao)* | |
| | 🏛️ **`bank_flex`** | ≥ 4.0s | **3 samples** | 0.1% / 0.01% | Standard banking, telecom *(Ngân hàng tiêu chuẩn)* | |
| | 📱 **`consumer`** | ≥ 2.0s | **3 samples** | 0.1% / 0.01% | Mobile apps, call centers, IoT *(Ứng dụng di động)* | |
|
|
| ### Scoring Pipeline |
|
|
| ``` |
| Audio → QA Gate (5 checks) → Windowed Embedding Extraction → AS-NORM (600K cohort) → QMF → Decision |
| ``` |
|
|
| | Component | Detail | |
| | --- | --- | |
| | **Embedding** | 512-dimensional voiceprint | |
| | **Score normalization** | AS-NORM with top-300 cohort matching | |
| | **Quality fusion** | Cohort Mean Fusion + Duration compensation | |
| | **Multi-enrollment** | Mean-aggregated across sessions | |
| | **Storage** | ~2 KB per enrolled speaker | |
|
|
| ### QA Gate — Mandatory Pre-Check |
|
|
| All audio **MUST** pass bank-grade QA before inference: |
|
|
| | Check | Threshold | Purpose | |
| | --- | --- | --- | |
| | Duration | ≥ 2s or ≥ 4s | Sufficient speech content | |
| | Clipping | < 0.1% | No distorted audio | |
| | RMS Energy | −45 to −5 dBFS | Proper recording level | |
| | Speech Ratio | ≥ 15% | Actual speech, not silence | |
| | SNR | ≥ 10 dB | Acceptable noise level | |
|
|
| *Without QA gate, tail performance degrades significantly. QA is mandatory for production.* |
| *(Không có QA gate, hiệu suất vùng đuôi giảm đáng kể. QA là bắt buộc.)* |
|
|
| --- |
|
|
| ## 🆚 Performance Context |
|
|
| ### Why These Numbers Matter |
|
|
| | Metric | BrightoSV V1.2 (4s/5e) | What It Means | |
| | --- | --- | --- | |
| | **EER 1.184%** | For every 1,000 verification attempts, ~12 are errors (combined false accept + false reject) | |
| | **FRR 3.18% @ FAR 0.1%** | At 1-in-1,000 impostor protection: only 3.2% genuine users need to retry | |
| | **FRR 6.65% @ FAR 0.01%** | At 1-in-10,000 impostor protection: only 6.7% genuine users need to retry | |
| | **Tail Gap +5.35** | The hardest 5% of genuine speakers are still **5.35 score units above** the strongest impostor region | |
|
|
| ### Scale Comparison |
|
|
| | Aspect | Typical Academic Eval | BrightoSV V1.2 | |
| | --- | --- | --- | |
| | Test pairs | ~100K–500K | **20,000,000** | |
| | Speakers | ~100–500 | **3,900+** | |
| | Score normalization | Often none | **AS-NORM (600K cohort)** | |
| | Multi-enrollment | Rarely tested | **3-enroll and 5-enroll** | |
| | QA gates | Rarely applied | **5-gate bank-grade** | |
| | Languages | Usually 1–2 | **9+ languages** | |
|
|
| --- |
|
|
| ## ⚙️ Technical Specifications |
|
|
| | Specification | Value | |
| | --- | --- | |
| | **Model Version** | V1.2 (Commercial SOTA) | |
| | **Parameters** | **316M** (High-Capacity Self-Supervised Backbone) | |
| | **Embedding Dimension** | **512** | |
| | **Input Sample Rate** | 16kHz (Auto-resampling supported) | |
| | **Input Formats** | WAV, FLAC, MP3, OGG, M4A | |
| | **Output** | 512D L2-normalized embedding | |
| | **Backends** | PyTorch, ONNX, HuggingFace | |
|
|
| ### 🚀 Hardware & Performance |
|
|
| | Specification | Value | |
| | --- | --- | |
| | **GPU Support** | NVIDIA T4, A10, A100, H100, L4 | |
| | **CPU Support** | Intel Xeon, AMD EPYC (via ONNX) | |
| | **Inference Latency** | **< 60ms** (GPU) / **< 400ms** (CPU ONNX) | |
| | **Model Size** | ~1.2 GB | |
| | **Batch Processing** | Supported | |
| | **Deployment** | Fully offline after initial download | |
|
|
| --- |
|
|
| ## 🌍 Application Scenarios |
|
|
| | Sector | Use Case | Recommended Level | |
| | --- | --- | --- | |
| | 🏦 **Banking & Finance** | Wire transfers, Voice Banking, Phone Banking | `bank_strict` | |
| | 🆔 **eKYC** | Customer onboarding, Remote identity verification | `bank_flex` | |
| | 🪙 **Crypto & FinTech** | Wallet protection, Transaction authorization | `bank_strict` | |
| | ✈️ **National Security** | Border control, Immigration screening | `bank_strict` | |
| | 🎧 **Call Centers** | Caller identity verification, Fraud prevention | `bank_flex` | |
| | 🏥 **Healthcare** | Patient identity, Telemedicine authentication | `bank_flex` | |
| | 📱 **Consumer Apps** | Voice login, Smart home, Voice assistants | `consumer` | |
|
|
| --- |
|
|
| ## 🔒 Privacy & Security |
|
|
| | Aspect | Implementation | |
| | --- | --- | |
| | **Audio Retention** | **Zero.** Audio processed in RAM, immediately discarded. *(Không lưu audio).* | |
| | **Voiceprint** | 512 numbers. **Non-reversible** — cannot reconstruct voice. *(Không thể tái tạo giọng nói).* | |
| | **Deployment** | On-premise or private cloud. No external calls. *(Triển khai nội bộ, không gọi ra ngoài).* | |
| | **Compliance** | **GDPR**, **PDPA**, **PCI-DSS** ready | |
| | **Data Sovereignty** | 100% local processing. Your data never leaves your infrastructure. | |
|
|
| --- |
|
|
| ## 🤝 Combined with Anti-Spoofing |
|
|
| For maximum security, deploy BrightoSV Speaker Verification **alongside** [BrightoSV Anti-Spoofing V1.5](https://huggingface.co/hatto-ai/BrightoSV-AntiSpoof-V1.5-Special-Edition): |
|
|
| ``` |
| Audio → Anti-Spoof Check (Is this a real voice?) → Speaker Verify (Is this the right person?) → Decision |
| ``` |
|
|
| | Layer | Model | Purpose | |
| | --- | --- | --- | |
| | **Layer 1** | Anti-Spoof V1.5 | Reject deepfakes, replay attacks, TTS | |
| | **Layer 2** | Speaker Verify V1.2 | Confirm speaker identity | |
|
|
| This dual-layer architecture provides **defense-in-depth**: even if a sophisticated deepfake passes liveness detection, it must still match the enrolled voiceprint — and vice versa. |
|
|
| *(Kiến trúc hai lớp cung cấp phòng thủ theo chiều sâu: ngay cả khi deepfake vượt qua kiểm tra liveness, vẫn phải khớp voiceprint — và ngược lại.)* |
|
|
| --- |
|
|
| ## 📈 Roadmap |
|
|
| | Version | Status | Highlight | |
| | --- | --- | --- | |
| | **V1.2** | 🟢 **Current** | Commercial SOTA — EER 1.184%, Bank-grade verified | |
| | V1.5 (LMF) | 🔵 In progress | Large Margin Fine-tuning — targeting FRR@0.01% < 5% | |
| | V2.0 | 🟡 Planned | Next-generation architecture | |
|
|
| --- |
|
|
| ## 📞 Access & Licensing |
|
|
| This model is **Private** and available exclusively for enterprise partners under NDA. |
| *(Model nội bộ, chỉ cung cấp cho đối tác Doanh nghiệp ký NDA.)* |
|
|
| ### Thương mại & Triển khai |
|
|
| - **License trọn gói** hoặc **qua API** |
| - Hỗ trợ tích hợp theo yêu cầu (triển khai, tối ưu hiệu năng, giám sát chất lượng) |
| - **Công ty Cổ phần SphinX** (sphinxjsc.com) được giao quyền **đóng gói, cung cấp API và phân phối** |
|
|
| ### Bản quyền & License |
|
|
| **Thương mại / Proprietary**. Việc sử dụng, phân phối lại hoặc tạo bản phái sinh cần có chấp thuận bằng văn bản từ BrighTO Technology. |
|
|
| ### Liên hệ |
|
|
| | Purpose | Contact | |
| | --- | --- | |
| | **Commercial Licensing** | `nguyen@brighto.ai`, `nghia@brighto.ai` | |
| | **API & Distribution** | `duc@sphinxjsc.com` (SphinX JSC) | |
| | **Technical Inquiries** | `nguyen@hatto.com` | |
|
|
| --- |
|
|
| <div align="center"> |
|
|
| ### 🏆 BrightoSV Speaker Verification V1.2 |
|
|
| **Commercial SOTA • Multilingual • Bank-Grade • Offline-Ready** |
|
|
| **EER 1.184%** · **20M Eval Pairs** · **600K Cohort** · **9+ Languages** |
|
|
| *Built in Vietnam 🇻🇳 • Engineered for the World 🌏* |
|
|
| </div> |
|
|
| --- |
|
|
| > *This model card refers to BrightoSV Speaker Verification V1.2 (Commercial SOTA Release). All benchmark results are verified on internal evaluation sets comprising 20,000,000 scored pairs across 3,900+ speakers with strict bank-grade QA methodology and AS-NORM score normalization using a 600,000+ speaker cohort.* |