Jegan022/Vocalis

VOXLESS: Real-Time Camera-Based Silent Speech-to-Personalized Voice Synthesis

An End-to-End Visual Silent Speech Interface (VSSI) for Non-Vocal Speakers

---

🔬 Central Research Hypothesis

A multi-scale visual articulatory representation combining lip appearance, temporal lip motion, jaw dynamics and lower-face motion, coupled with phoneme-viseme multi-task supervision and speaker-adaptive neural speech synthesis, can produce lower-latency and more intelligible personalized speech than conventional lip-to-text-to-TTS pipelines for non-vocal speakers.

---

🏛️ System Architecture

                       RGB CAMERA (30–60 FPS)
                                 │
                                 ▼
                     Face Tracking & Alignment
                (MediaPipe Face Mesh + Affine Warp)
                                 │
                                 ▼
                    Multi-Scale Articulatory ROI
          ┌──────────────────────┼──────────────────────┐
          ▼                      ▼                      ▼
     Lip Region              Jaw & Chin             Cheeks & Contour
     (96 × 96)              (112 × 112)                 (64 × 64)
          │                      │                      │
          └──────────────────────┼──────────────────────┘
                                 ▼
                   Multi-Stream Spatial Encoder
               (ConvNeXt-VSSI / 2D-3D Hybrid Frontends)
                                 │
                                 ▼
               Cross-Articulatory Attention Fusion
              (Lip Appearance + Dynamics + Jaw + Pose)
                                 │
                                 ▼
                   Temporal Articulatory Encoder
               (Conformer / Transformer / BiLSTM)
                                 │
                                 ▼
                     Visual Speech Representation
                                 │
                 ┌───────────────┴───────────────┐
                 ▼                               ▼
       Viseme Decoder (Auxiliary)     Phoneme Decoder (CTC)
       (Fisher/Jeffers Visemes)       (ARPAbet / IPA Tokens)
                 │                               │
                 └───────────────┬───────────────┘
                                 ▼
                  Multi-Task Articulatory Loss
           L = λ₁L_ctc + λ₂L_phoneme + λ₃L_viseme + λ₄L_lang
                                 │
                                 ▼
                 Streaming Prefix Beam Search + LM
               + Confidence & Uncertainty Estimation
                                 │
                 ┌───────────────┴───────────────┐
                 ▼                               ▼
           [Pipeline A: Cascaded]         [Pipeline B: Direct]
           Reconstructed Text/Phonemes    Articulatory Mel-Gen
                 │                               │
                 ▼                               │
           Speaker Adapter                       │
          (LoRA / Emb Conditioning)              │
                 │                               │
                 ▼                               ▼
        Personalized Neural TTS          Acoustic Vocoder
        (Acoustic Model + HiFi-GAN)       (HiFi-GAN / Mel)
                 │                               │
                 └───────────────┬───────────────┘
                                 ▼
                     Real-Time Audio Buffer
                   (<300 ms End-to-End Latency)

---

🚀 Key Innovations

1. Multi-Scale Articulatory Decomposition: Processes not just lips ($96 \times 96$), but mandibular excursion ($112 \times 112$), cheek dynamics ($64 \times 64$), and 3D head pose simultaneously.
2. Multi-Task Phoneme-Viseme Supervision: Resolves homophene visual ambiguities by co-learning viseme clusters (Fisher & Neti) alongside CTC phonemes and text.
3. Dual-Track Personalized Synthesis:
   • Pipeline A (Cascaded): Visual $\to$ Reconstructed Text $\to$ Speaker-conditioned TTS.
   • Pipeline B (Direct Visual-to-Speech): Visual Embeddings $\to$ Direct Mel-Spectrogram $\to$ Vocoder.
4. Rapid Speaker Personalization: Low-Rank Adaptation (LoRA) and few-shot calibration calibrate new speakers in under 3 minutes.
5. Confidence-Aware Assistive Gating: Evaluates CTC posterior probabilities and entropy to prevent hallucinations, triggering polite fallback repetitions ("Low confidence. Please repeat.") when uncertain.
6. Sub-300ms Real-Time Streaming: Asynchronous frame grabber, visual VAD, and chunk-level streaming prefix beam decoder.

---

📦 Installation

# 1. Clone repository and navigate
git clone https://github.com/vocalis/voxless.git
cd voxless

# 2. Create isolated virtual environment
python -m venv .venv
.venv\Scripts\activate  # Windows
# source .venv/bin/activate  # Linux/macOS

# 3. Install PyTorch with CUDA 12.x support
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu124

# 4. Install dependencies
pip install -r requirements.txt
pip install -e .

---

🎮 Usage & CLI

1. Run Real-Time Camera Demonstration

python -m voxless.cli demo --camera 0 --pipeline pipeline_a

Keyboard controls:

• q: Quit application
• r: Reset beam decoder history
• s: Toggle between Pipeline A (Cascaded) and Pipeline B (Direct)

2. Calibrate & Enroll a New Speaker

python -m voxless.cli enroll --speaker-id spk_01 --speaker-name "Participant"

3. Record Custom Dataset (VOXLESS-INDIA)

python -m voxless.cli collect --speaker-id spk_01 --num-prompts 5

4. Run Scientific Ablation Study Matrix

python -m voxless.cli ablate

5. Compare with Mandatory Baselines

python -m voxless.cli benchmark

6. Launch Interactive Web Dashboard

streamlit run voxless/app/web_dashboard.py

---

🧪 Testing

Run the automated test suite covering vision, models, decoders, adaptation, synthesis, and streaming latency:

pytest tests/ -v

---

🛡️ Ethical Safeguards & Consent Gate

Voice cloning and personalized synthesis require cryptographic token verification (EthicalConsentGate). Voice profiles cannot be synthesized without participant-signed consent.