Course Objective & Scope
Human conversational turn transitions happen in roughly 200–300 milliseconds; if a voice assistant cuts the user off during a mid-sentence pause to think, or waits two seconds before replying, conversational rhythm collapses. This specialised course covers turn-taking algorithms and end-to-end latency engineering.
Participants combine acoustic Voice Activity Detection (Silero VAD) with semantic end-of-turn prediction models, implement echo cancellation, false-barge-in suppression, sentence streaming and speculative execution, and deliver a comprehensive latency and quality measurement plan.
Who Should Attend?
- Senior engineers building voice assistants and contact centre AI
- Speech processing, audio signal and real-time systems developers
- Technical leads accountable for voice interaction quality
- Prerequisite: Python and hands-on experience with voice AI pipelines (STT/LLM/TTS or S2S)
Key Takeaways
- Combine acoustic VAD with semantic endpointing models that judge whether a thought is complete
- Prevent premature cut-offs when callers pause while dictating numbers, addresses or complex thoughts
- Distinguish backchannels ('uh-huh', 'right') and background noise from genuine barge-in interruptions
- Instrument and optimise the millisecond budget across VAD, STT, LLM TTFT, TTS TTFB and network transport
- Reduce perceived latency using speculative execution and conversational micro-acknowledgements
Curriculum
Conversational Dynamics and the Millisecond Budget
- Human turn-taking, mid-utterance pauses and overlap mechanics
- Decomposing Voice-to-Voice latency across every pipeline stage
Acoustic VAD and Semantic Endpointing
- Tuning Silero VAD thresholds and the limits of silence-only detection
- Lightweight turn-detection models using transcript and prosody cues
Smart Barge-In and False-Interruption Suppression
- Acoustic Echo Cancellation (AEC) to stop the agent interrupting itself
- Separating listener backchannels ('uh-huh', 'okay') from true interruptions
TTFT / TTFB Optimisation and Speculative Execution
- Prompt caching, fast model routing and streaming first clauses to TTS early
- Speculatively firing RAG/API lookups before final end-of-turn confirmation
Perceived Latency Design and Measurement Plan
- Natural conversational acknowledgements and expectation setting
- Benchmarking turn-taking error rates and p50/p95 latency on recorded calls
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