Test barge-in end-to-end with Cekura: measure interruption latency, TTS overrun, ASR transcription accuracy and recovery across ASR engines, noise conditions, and automated test scenarios.
Test and compare ASR barge-in performance with Cekura’s voice testing platform. Measure interruption latency, TTS overrun, transcription accuracy, and recovery across real-world voice scenarios.
Barge-in is one of the fastest ways a voice experience fails.
Users talk over the AI. The system hesitates. Audio overlaps. Transcripts degrade. Conversations derail. Testing barge-in properly means validating how your ASR, TTS, and voice infrastructure behave under real interruption pressure, not just whether an interruption happened. Cekura is built to test barge-in behavior across different ASR engines, voice stacks, and infrastructure setups using automated, repeatable voice simulations.
Barge-in only matters if user speech is detected while the AI is still talking. Cekura detects overlap at the audio level and explicitly labels these moments as interruption events. Detection works for very short interruptions under 300 ms and for softer, low-volume speech within ASR limits.
It is not enough to know the user interrupted. You need to know how long the AI kept talking afterward. Cekura measures interruption overrun in milliseconds, capturing exactly how long TTS continued after user speech began.
Overlapping speech is transcribed and retained. Teams can isolate interrupted runs and compare transcription quality against clean runs using custom or Python-based metrics to quantify degradation.
After barge-in, the conversation must resume cleanly. Cekura evaluates recovery through instruction following, response consistency, and workflow success. Any deviation is timestamped at the moment state breaks.
Latency, interruption counts, overrun duration, and timing data are available as numeric metrics. False and missed barge-in rates can be derived across scenarios. ASR WER deltas can be computed directly from transcripts when needed.
Cekura supports duplex conversations with overlapping send and receive paths through telephony, WebRTC, and WebSocket integrations including VAPI, Retell, LiveKit, Pipecat, and ElevenLabs.
Cekura does not require a specific AEC model. Stereo recordings are recommended so overlap and interruption events can be distinguished cleanly regardless of provider.
Voice activity detection behavior is evaluated based on results, not configuration. Whether the ASR missed speech or triggered false interruptions shows up directly in interruption and silence metrics.
Partial ASR behavior is inferred through timing. Speech onset, detection, TTS stop, and final transcript availability are all timestamped for precise latency analysis.
Every barge-in test captures event-level timestamps that allow you to derive key moments and build comparable latency distributions across engines and versions.
From this, teams get distributions like mean, P50, and P90 for interruption latency and recovery time across ASR engines and versions.
Barge-in rarely happens cleanly. Users interrupt mid-sentence, at prosodic peaks, or with varying speech styles. Cekura supports a wide range of interruption types so you can measure recognition quality drops and compare engines.
This makes it possible to measure how much recognition quality drops when interruptions occur and which ASR engines degrade least.
Real calls include cafés, offices, background chatter, and silence artifacts. Cekura simulates realistic ambient conditions to reveal how noise impacts interruption detection and recovery.
False interruptions and missed barge-ins can be inferred directly from noise-heavy and expected-interruption scenarios using interruption counts, overrun timing, and silence metrics.
Scenarios are reusable and can be triggered via CI, cron jobs, or A/B test runs to catch regressions early.
Each scenario can be replayed across ASR engines, prompts, models, and infrastructure changes with identical inputs.
This makes ASR benchmarking concrete instead of anecdotal.
If your voice agent speaks over users, misses interruptions, or loses state after overlap, you will not see it through basic logging. Cekura lets teams test barge-in the way users actually experience it, across ASR engines, noise conditions, and real conversation dynamics, before those failures hit production.
Learn more about Cekura's capabilities at https://cekura.ai
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