Namo-Turn-Detection-v1: Semantic Turn Detection for AI Voice Agents

Turn-taking — the ability to know exactly when a user has finished speaking — is the invisible force behind natural human conversation. Yet most voice agents today rely on Voice Activity Detection (VAD) or fixed silence timers, leading to premature cut-offs or long, robotic pauses.

We introduce NAMO Turn Detector v1 (NAMO-v1), an open-source, ONNX-optimized semantic turn detector that predicts conversational boundaries by understanding meaning, not just silence. NAMO achieves <19 ms inference for specialized single-language models, <29 ms for multilingual, and up to 97.3 % accuracy — making it the first practical drop-in replacement for VAD in real-time voice systems.

1. Why Existing Approaches Break Down

Most deployed voice agents use one of two approaches:

• Silence-based VAD: very fast and lightweight but either interrupts users mid-sentence or waits too long to be sure they’re done.
• ASR endpointing (pause + punctuation): better than raw energy detection, but still a proxy; hesitations and lists often look “finished” when they’re not, and behavior varies wildly across languages.

Both approaches force product teams into a painful latency vs. interruption trade-off: either set a long buffer (safe but robotic) or a short one (fast but rude).

2. NAMO’s Semantic Advantage

NAMO replaces “silence as a proxy” with semantic understanding. The model looks at the text stream from your ASR and predicts whether the thought is complete. This single change brings:

• Lower floor transfer time (snappier replies) without raising false cut-offs.
• Multilingual robustness: one model works across 23+ languages, no per-language tuning.
• Production latency: ONNX-quantized models run in <30 ms on CPU/GPU with almost no accuracy loss.
• Observability & tuning: you can get calibrated probabilities and adjust thresholds for “fast vs. safe.”

Namo uses Natural Language Understanding to analyze the semantic meaning and context of speech, distinguishing between:

• Complete utterances (user is done speaking)
• Incomplete utterances (user will continue speaking)

Key Features

• Semantic Understanding: Analyzes meaning and context, not just silence
• Ultra-Fast Inference: <19ms for specialized models, <29ms for multilingual
• Lightweight: ~135MB (specialized) / ~295MB (multilingual)
• High Accuracy: Up to 97.3% for specialized models, 90.25% average for multilingual
• Production-Ready: ONNX-optimized for real-time, enterprise-grade applications
• Easy Integration: Standalone usage or plug-and-play with VideoSDK Agents SDK

3. Performance Benchmarks

Latency & Throughput

Using ONNX quantization, NAMO moves from 61 ms to 28 ms inference (multilingual) and 38 ms to 14.9 ms (specialized).

• Relative speedup: up to 2.56×
• Throughput: doubled (35.6 to 66.8 tokens/sec)

Accuracy Impact

Quantization preserves accuracy:

Confusion matrices show virtually unchanged true/false rates before and after quantization.

Language Coverage

Average multilingual accuracy: 90.25 %
Specialized single-language models: 97.3 % (Turkish/Korean), >93 % Hindi, Japanese, German.

5. Impact on Real-Time Voice AI

With NAMO you get:

• Snappier responses without the “one Mississippi” delay.
• Fewer interruptions when users pause mid-thought.
• Consistent UX across markets without tuning for each language.
• Cost-effective scaling — works with any STT and runs efficiently on commodity servers.

6. Impact on Real-Time Voice AI

Namo offers both specialized single-language models and a unified multilingual model

* Latency measured after quantization on target inference hardware.

Multilingual Model (Recommended):

• Model: Namo-Turn-Detector-v1-Multilingual
• Base: mmBERT
• Languages: All 23 supported languages
• Inference: <29ms
• Size: ~295MB
• Average Accuracy: 90.25%
• Model Link: Namo Turn Detector v1 - MultiLingual

Performance Benchmarks for Multilingual Model

Evaluated on 25,000+ diverse utterances across all supported languages.

Average Accuracy: 90.25% across all languages

Specialized Single-Language Models

• Architecture: DistilBERT-based
• Inference: <19ms
• Size: ~135MB each

Try It Yourself!

We’ve provided an inference script to help you quickly test these models. Just plug it in and start experimenting!

• Hugging Face Models: https://huggingface.co/videosdk-live/models
• Github Repo Link: https://github.com/videosdk-live/NAMO-Turn-Detector-v1/tree/main

Integration with VideoSDK Agents

For seamless integration into your voice agent pipeline:

from videosdk_agents import NamoTurnDetectorV1, pre_download_namo_turn_v1_model

# Download model files (one-time setup)
# For multilingual (default):
pre_download_namo_turn_v1_model()

# For specific language:
# pre_download_namo_turn_v1_model(language="en")

# Initialize turn detector
turn_detector = NamoTurnDetectorV1()  # Multilingual
# turn_detector = NamoTurnDetectorV1(language="en")  # English-specific

# Add to your agent pipeline
from videosdk_agents import CascadingPipeline

pipeline = CascadingPipeline(
    stt=your_stt_service,
    llm=your_llm_service,
    tts=your_tts_service,
    turn_detector=turn_detector  # Namo integration
)

7. Training & Testing

Each model includes Colab notebooks for training and testing:

• Training Notebooks: Fine-tune models on your own datasets
• Testing Notebooks: Evaluate model performance on custom data

Visit individual model pages for notebook links:

• Multilingual Training Notebook
• Specialised Model Training Notebook
• Testing Notebook
• Inference Script

Looking Ahead: Future Directions

• Multi-party turn-taking detection: deciding when one speaker yields to another.
• Hybrid signals: combine semantics with prosody, pitch, silence, etc.
• Adaptive thresholds & confidence models: dynamic sensitivity based on conversation flow.
• Distilled / edge versions for latency-constrained devices.
• Continuous learning / feedback loop: let models adapt to usage patterns over time.

Conclusion

NAMO-v1 turns a long-standing bottleneck — turn-taking — into a solved engineering problem. By combining semantic intelligence with real-time speed, it finally allows voice AI systems to feel human, fast, and globally scalable.

Citation

@software{namo2025,
  title={Namo Turn Detector v1: Semantic Turn Detection for Conversational AI},
  author={VideoSDK Team},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/collections/videosdk-live/namo-turn-detector-v1-68d52c0564d2164e9d17ca97}
}