ProsodyAIProsody intelligence
for voice pipelines.
An SSM-based model that runs parallel to your ASR, extracting prosodic features and streaming chunk-level emotion and VAD estimates. Capacity and latency depend on deployment sizing.
Output format
Per-utterance emotion classification with valence-arousal-dominance vectors, word-level alignment, and vertical-specific state mappings. Streamed via WebSocket or polled via REST.
Yeah, hi. This is the third time I'm calling about this billing issue.
{ "emotion": "frustrated", "confidence": 0.84, "vad": [0.3, 0.65, 0.58], "vertical_state": "impatient", "escalation_risk": "medium", "prosody": { f0: 198, energy: -9.8, ...28 dims }, "predictions": { ... } }
Infrastructure
Designed for production voice pipelines. Streaming inference, deterministic outputs, horizontal scaling.
Streaming-capable inference
Chunk-level output for voice workflows. Latency should be measured against the actual Baseten/API deployment.
32-dim feature extraction
F0, energy, jitter, shimmer, HNR, MFCCs, spectral centroid, speech rate, pause duration. Extracted per frame.
Multi-head output
Emotion softmax, VAD regression, and vertical-specific state mapping from a single forward pass.
Drop-in integration
REST and WebSocket APIs. Python and JavaScript SDKs. Runs parallel to any STT provider.
On-premise available
Deploy on your infrastructure. Audio never leaves your VPC. SOC 2 Type II compliant.
Capacity tested per deployment
Throughput depends on model size, GPU allocation, cold starts, API concurrency, chunk duration, and downstream ASR.
Forward prediction
A causal GRU that consumes per-utterance ProsodySSM outputs and predicts session-level outcomes at every timestep. 8 predictive heads, O(1) incremental updates, confidence scaling with sequence length.
Confidence scales with sequence length
c = min(1.0, 0.3 + 0.7 · n/W). Predictions sharpen as the GRU accumulates utterance history.
will_escalate
Binary sigmoid head. P(escalation) = 0.73 after 3 utterances. Supervised against session-level escalation labels.
final_csat
Regression head, range [1.0, 5.0]. Predicts final CSAT at every timestep. MSE loss with temporal weighting.
churn_risk
Binary sigmoid head. P(churn within 30d) derived from prosodic trajectory patterns. Trains on CRM outcome data.
recommended_tone
6-class softmax head. Outputs empathetic | calm | enthusiastic | professional | reassuring | apologetic.
Architecture
4-layer Mamba SSM with S4D diagonal state matrices. Prosodic and phonetic features fused into a 256-dim representation, processed with O(n) recurrence. Multi-head output: emotion softmax + VAD regression + vertical state mapping.
Model Pipeline
Feature Extraction
Benchmark Results
Trained on CREMA-D, RAVDESS, TESS, and Orpheus corpora with speaker-disjoint validation. ProsodySSM outperforms transformer baselines (+8.3% WA) while maintaining O(n) complexity via S4D-Lin initialization. Human inter-annotator agreement on SER is typically 60-70%.
Capabilities
Single model, vertical-adaptive output. Fine-tune on your distribution. Configure thresholds per deployment.
LoRA fine-tuning
Low-rank adapters on the SSM blocks. Train on your labeled data without retraining the base model. Outcome-weighted loss with active learning sample selection.
- Two-phase training pipeline
- 70/30 original/feedback mix ratio
- Continuous improvement from production
Alert thresholds
Define per-vertical alert rules via alert_thresholds in VerticalConfig. Webhook dispatch on threshold breach. Composable with any event bus.
- Per-vertical threshold config
- Webhook dispatch on trigger
- Kafka topic routing
Outcome feedback loop
Submit session outcomes (CSAT, escalation, churn) via feedback API. Active learning selects high-value samples. Model improves on your production distribution.
- CRM outcome ingestion
- Active learning selection
- Contradiction-weighted retraining
Parallel ASR deployment
Runs as a sidecar to your STT pipeline. Consumes the same audio stream. Output aligns to word timestamps from any ASR provider.
- Provider-agnostic
- Word-level timestamp alignment
- No pipeline changes required
Vertical taxonomies
8 predefined verticals with domain-specific state enums, metrics, and alert thresholds. Single base model, vertical-adaptive output via VerticalConfig.
- Contact center, healthcare, sales, legal...
- VAD-based state disambiguation
- Per-vertical confidence thresholds
Stream prosodic signals to your LLM context window. The agent adapts tone and strategy based on real-time VAD vectors.
Batch process recordings. Output emotion timelines, session-level predictions, and vertical-specific metrics for every conversation.
Kafka event streaming on threshold breach. Route to supervisors, trigger script changes, or dispatch webhooks — all at utterance boundaries.
Quick start
Extract prosodic features. Run inference. Map to your vertical.
from prosody import ProsodyClient
client = ProsodyClient(api_key="your-key")
result = client.analyze(
audio_file="recording.wav",
features=["emotion", "prosody"]
)
print(result.emotion) # "happy"
print(result.valence) # 0.72
print(result.arousal) # 0.65import { Prosody } from '@prosody/sdk';
const client = new Prosody({ apiKey: 'your-key' });
const result = await client.analyze({
audio: audioBlob,
features: ['emotion', 'prosody']
});
console.log(result.emotion); // "happy"
console.log(result.valence); // 0.72
console.log(result.arousal); // 0.65ProsodyCRM
Management plane for your ProsodySSM deployment. API keys, vertical configuration, transcript analysis, outcome feedback, and model fine-tuning.
API Keys
Generate API keys to integrate Prosody directly into your own apps, pipelines, or services. Full programmatic access.
- REST & WebSocket APIs
- Python & JS SDKs
- Rate limits & usage tracking
Integrations
Connect to AWS Transcribe, Salesforce, HubSpot, Zendesk, and more. One-click OAuth setup.
- Cloud storage sync
- CRM connectors
- Webhook events
Transcript Analysis
Upload recordings or sync from cloud storage. View emotion timelines, word-level annotations, and summaries.
- Batch processing
- Emotion timeline
- Export to CRM
Custom Taxonomies
Define emotion states for your industry. Map base emotions to domain-specific labels with custom thresholds.
- Industry presets
- State mapping
- Threshold tuning
Analytics
Track emotion trends over time. Monitor API usage, identify patterns, and export reports.
- Usage metrics
- Trend analysis
- CSV/PDF export
Team Management
Invite team members with role-based permissions. Admin, member, and viewer roles available.
- SSO support
- Role permissions
- Audit logs
Model Fine-tuning
Train custom LoRA adapters on your data. Upload labeled samples, fine-tune on GCP, and deploy your own model.
- LoRA fine-tuning
- Hosted on GCP
- Your data, your model
Event Orchestration
Kafka-powered event streaming for emotion pipelines. Event latency depends on the deployed broker, consumers, and network path.
- Kafka topics per event type
- Dead letter queues
- Event replay & audit
Start building
Free tier available. API keys provision instantly. Load test your deployment before publishing throughput targets.
Contact
Integration architecture, vertical configuration, on-premise deployment, or custom LoRA training. Reach out.