Speech latency optimisation

When this helps

For teams running phone agents, conversational products, transcription or audio workflows where users wait between speaking and hearing a response. Use this when first audio feels slow, complete answers take too long, interruptions behave poorly, or latency rises with concurrent calls.

Trace the path from end of turn to response

Measure the stages after a user turn: end-of-turn detection, automatic speech recognition (ASR), language model processing and text-to-speech (TTS). Identify where time is spent before the system starts speaking and where delay accumulates afterward.

Track first-audio latency separately from time to a complete answer. A system may start quickly but finish slowly, or wait too long before first audio. These measures describe different parts of the user experience.

Keep transcription and conversation quality in view

Use representative speech and assess recognition quality alongside timing. A faster transcript that changes important words, loses context or increases downstream mistakes may not improve the result for the user.

Record errors across the full path, including incomplete responses and failures between services. Compare changes on the same audio and task conditions, so latency gains are considered with transcription quality and successful conversation outcomes.

Examine interruptions and network effects

For interactive voice systems, review how the product handles a user speaking over generated audio, stopping playback and starting the next turn. Measure how quickly control returns to the conversation and whether partial speech or generated output leaves the system in the wrong state.

Include network and service boundaries in the trace. Delays may come from transport, buffering or coordination between components, so tuning one model alone may not address the wait experienced by the user.

Test under the call patterns that matter

Compare response timing at representative levels of concurrent calls. Observe both typical and slower responses. Note when queues form or errors increase as demand changes; a single quiet request does not describe behaviour during busier periods.

Choose experiments from the measured bottlenecks, then repeat the comparison with quality and error checks. Scope, price and timeline are agreed around the workload and its requirements before implementation.

What you receive

• A stage-by-stage timing baseline across end-of-turn detection, ASR, LLM and TTS.
• Separate measurements for first audio and complete-answer time on representative interactions.
• A review of transcription quality, full-path errors, interruptions and network boundaries.
• Selected experiments and, where agreed, implementation with monitoring and rollback guidance.

Common questions