Deepgram outlines blueprint for sub-500ms real-time sentiment analysis on live audio

Deepgram, the custom voice agent specialist, has published a technical guide explaining how to build real-time sentiment analysis systems for streaming audio, defining a production target of approximately 500 milliseconds end-to-end latency from raw audio input to an actionable sentiment score.

According to the company, meeting that budget requires optimisation across the entire pipeline. In typical production deployments, 100 to 200 milliseconds are allocated to speech-to-text, 150 to 200 milliseconds to sentiment inference, and 50 to 100 milliseconds to network delivery. Because these stages are sequential, the company said speech-to-text latency effectively sets the floor for the system, leaving little margin for inefficiency elsewhere.

To manage the competing demands of immediacy and accuracy, Deepgram recommends a two-tier buffering architecture. Audio should be streamed in short 50 to 100-millisecond chunks for transcription, minimising delay in converting speech to text. At the same time, transcribed text should be accumulated into longer 800 to 1,200-millisecond windows for sentiment analysis. These longer windows provide sufficient linguistic context for reliable sentiment detection without blowing the latency budget.

The guide advises overlapping sentiment analysis windows by around 10 to 15%. This overlap helps smooth transitions between windows and reduces the risk of missing sentiment shifts that occur at boundaries. Rather than triggering sentiment scoring at fixed time intervals, Deepgram recommends firing inference on “utterance end” events, when a speaker finishes a thought. This approach aligns analysis with natural speech patterns and reduces unnecessary computation.

Speaker diarisation, the process of identifying and separating speakers within an audio stream, is described as essential for production-grade systems. Without it, sentiment scores can be conflated across participants. Deepgram said teams should target a Diarization Error Rate below 10% to produce reliable per-speaker sentiment outputs.

The guide also addresses operational resilience. For live systems using WebSockets, Deepgram recommends maintaining a local rolling audio buffer of two to five seconds. This allows the client to replay recent audio after a transient disconnection. To avoid double-counting, systems should track timestamps and deduplicate replayed segments. Reconnection logic should use exponential backoff with jitter to prevent thundering-herd effects during network instability.

Related reading

• HBO Max expands direct-to-consumer streaming across eight new European and regional markets
• Fanatics and OBB Media launch Fanatics Studios for global sports entertainment
• Usercentrics acquires MCP Manager to extend AI governance and consent controls

On model performance, Deepgram said sentiment inference must be aggressively optimised to stay below roughly 150 milliseconds. Techniques such as model distillation or hardware acceleration may be required. On the transcription side, the company cited its Nova speech-to-text models as achieving sub-300 millisecond latency, which it said makes real-time sentiment analysis feasible within the overall budget.

The guide frames real-time sentiment analysis as an engineering problem of latency budgeting and system design, rather than model accuracy alone, and is aimed at teams building live customer service, monitoring and conversational AI applications.

The Recap

• Deepgram published a guide for real-time streaming sentiment.
• Pipeline budgets speech-to-text, sentiment inference, and network delivery.
• Maintain a two to five second rolling audio buffer.