Voice Safety Detection
Detect harassment, profanity, hate speech, and illegal content in audio. AI-powered voice content moderation for calls, podcasts, and streaming.
Bynn Voice Safety Detection
The Bynn Voice Safety Detection model analyzes audio content to identify unsafe speech including harassment, profanity, discrimination, and other policy-violating content. This multilingual model enables real-time moderation of voice chat, audio messages, and spoken content across platforms.
The Challenge
Voice communication has become central to online interaction—gaming platforms, social apps, virtual meetings, and live streaming all rely on real-time voice chat. But voice also enables abuse that's harder to moderate than text. Harassment, hate speech, and inappropriate content spoken aloud evade traditional text-based filters entirely.
The scale is staggering. Popular platforms process millions of minutes of voice chat daily. Human moderation cannot keep pace—by the time a report is reviewed, the damage is done and the perpetrator has moved on. Victims, often young users, experience harassment in real-time with no recourse. Platforms need automated detection that can identify toxic speech as it happens, enabling immediate intervention.
Multilingual support is essential. Global platforms serve users speaking dozens of languages, and toxic content exists in all of them. English-only moderation leaves non-English speakers unprotected and allows bad actors to evade detection simply by switching languages.
Model Overview
The Bynn Voice Safety Detection model performs multilabel classification across six toxicity categories, supporting eight languages. Trained on extensive real-world voice chat data with both automated and human labels, the model achieves robust detection while maintaining low false positive rates critical for user experience.
Achieving 86.5% accuracy, the model processes audio at scale—capable of handling thousands of requests per second for real-time moderation of live voice communications.
How It Works
The model employs advanced speech understanding to analyze audio content:
- Direct audio analysis: Processes raw audio at 16kHz without requiring transcription
- Multilabel classification: Detects multiple violation types simultaneously in a single pass
- Probability scoring: Returns calibrated confidence scores for each category
- Optimized for voice chat: Best performance on 15-second segments typical of conversational speech
Response Structure
The API returns a structured response containing:
- classifications: Object with scores for each toxicity category (0.0-1.0)
- flagged_categories: Array of categories exceeding the detection threshold
- is_toxic: Boolean indicating if any category was flagged
Detection Categories
The model detects six categories of unsafe content:
| Category | Description |
|---|---|
| Discrimination | Hate speech targeting race, ethnicity, religion, gender, sexuality, disability, or other protected characteristics |
| Harassment | Bullying, personal attacks, threats, intimidation, and targeted abuse |
| Sexual | Sexually explicit content, sexual solicitation, and inappropriate sexual references |
| Illegal & Regulated | Discussion of illegal activities, drug use, weapons, and regulated content |
| Dating & Romantic | Romantic solicitation and dating-related content (important for platforms with minor users) |
| Profanity | Vulgar language, obscenities, and strong profanity |
Supported Languages
The model supports eight languages with optimized detection for each:
- English - Primary language with extensive training data
- Spanish - Full support for Spanish-speaking regions
- German - European German language support
- French - French language detection
- Portuguese - Brazilian and European Portuguese
- Italian - Italian language support
- Korean - Korean language detection
- Japanese - Japanese language support
Performance Metrics
| Metric | Value |
|---|---|
| Detection Accuracy | 86.5% |
| Average Response Time | 2,500ms |
| Max File Size | 10MB |
| Supported Formats | MP3, WAV, OGG, AAC, M4A, FLAC |
| Optimal Segment Length | 15 seconds |
| Sample Rate | 16kHz |
Use Cases
- Gaming Platforms: Real-time moderation of in-game voice chat to protect players from harassment and toxic behavior
- Social Apps: Monitor voice messages and audio posts for policy violations
- Live Streaming: Detect inappropriate content in live audio streams before broadcast
- Virtual Events: Ensure safe communication in virtual meetings, conferences, and social spaces
- Child Safety: Protect minor users from predatory behavior, grooming, and inappropriate content
- Customer Service: Monitor call recordings for harassment, discrimination, or compliance violations
Known Limitations
Important Considerations:
- Language Coverage: Best performance in supported languages; accuracy may vary for dialects, accents, or code-switching
- Audio Quality: Background noise, music, or poor audio quality may reduce detection accuracy
- Context Sensitivity: Some content may be toxic in one context but acceptable in another (e.g., quoting, educational discussion)
- Segment Length: Very long audio segments may have degraded accuracy; optimal performance at ~15 seconds
- Evolving Language: New slang, coded language, and emerging toxic phrases may require model updates
Disclaimers
This model provides probability scores, not definitive content judgments.
- Threshold Tuning: Adjust detection thresholds based on your platform's tolerance for false positives vs. false negatives
- Human Review: Severe violations and appeals should be reviewed by trained moderators
- Context Matters: Consider conversation context when taking moderation actions
- User Experience: Balance safety with user experience; overly aggressive moderation can harm legitimate communication
- Complementary Tools: Use alongside text moderation, behavioral analysis, and reporting systems for comprehensive safety
Best Practice: Implement tiered responses—warnings for borderline content, temporary mutes for clear violations, and escalation to human review for severe or repeated offenses.
API Reference
Input Parameters
Detects unsafe audio content including hate speech, harassment, profanity, and other toxicity
audio_urlstringRequiredURL of audio file to analyze for unsafe content
https://example.com/audio.mp3Response Fields
Audio safety analysis with multi-category classification
is_unsafebooleanTrue if unsafe content detected
falsediscrimination_probabilityfloatProbability of discrimination/hate speech (0.0-1.0)
0.02harassment_probabilityfloatProbability of harassment content (0.0-1.0)
0.03sexual_probabilityfloatProbability of sexual content (0.0-1.0)
0.01illegal_probabilityfloatProbability of illegal activity content (0.0-1.0)
0.01dating_probabilityfloatProbability of inappropriate dating content (0.0-1.0)
0.02profanity_probabilityfloatProbability of profanity (0.0-1.0)
0.05max_probabilityfloatHighest probability across all categories (0.0-1.0)
0.05top_categorystringCategory with highest probability
safeComplete Example
Request
{
"model": "voice-safety-detection",
"audio_url": "https://example.com/audio.mp3"
}Response
{
"success": true,
"data": {
"is_unsafe": false,
"discrimination_probability": 0.02,
"harassment_probability": 0.03,
"sexual_probability": 0.01,
"illegal_probability": 0.01,
"dating_probability": 0.02,
"profanity_probability": 0.05,
"max_probability": 0.05,
"top_category": "safe"
}
}Additional Information
429 HTTP error code along with an error message. You should then retry with an exponential back-off strategy, meaning that you should retry after 4 seconds, then 8 seconds, then 16 seconds, etc.Ready to get started?
Integrate Voice Safety Detection into your application today with our easy-to-use API.