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To make a Gujarati phone agent feel faster, measure the caller’s pause from the end of their speech to the agent’s first audible reply, then identify which pipeline stage is adding the delay. Tune turn detection, recognition, model generation, speech synthesis, and phone-network delivery separately. Keep replies concise, and test with real Gujarati phone speech—including Gujarati-English code switching—so lower latency does not come at the cost of interruptions or misunderstandings.
Measure the pause the caller actually hears
Use caller-side latency: the time from the end of the caller’s turn to the first audible agent audio. A server-side time-to-first-byte (TTFB) can be useful, but it may omit the wait for turn detection and the network path to the caller’s phone.
For a baseline, make calls over the phone route your customers use. Keep the line, caller script, and turn conditions consistent, and record the end of each caller turn and the start of agent audio. Compare the distribution of results—not just one call—including the median and slow-tail turns. OpenBenchmarks’ 2026 comparison reports a lowest median caller-side latency of 1,296 ms among five platforms it measured using recorded real phone calls. That is a general platform result, not a Gujarati-specific target or proof of response quality; the page also cautions that measurements with different clock boundaries are not directly comparable. OpenBenchmarks’ 2026 latency comparison
There is no evidence here for a universal delay threshold that makes every Gujarati caller feel a system is responsive. Treat any target as a product decision and evaluate it alongside comprehension, turn-taking, and task completion.
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Trace the pipeline to find where time goes
Log stage timings for each turn, then compare them with the caller-side pause. Microsoft’s voice-agent guidance recommends monitoring time to first audio and stage timing; its observability documentation covers model token timing, automatic speech recognition (ASR) latency and real-time factor, and text-to-speech (TTS) first- and last-audio timing. Microsoft’s voice-agent best practices · Microsoft’s observability documentation
- Turn detection: How long the system waits before deciding the caller has finished.
- ASR: Time to recognize the caller’s speech, and whether errors trigger repair turns.
- Model: Time until the model begins producing a reply, including any tool or retrieval work.
- TTS: Time until the first speech audio is ready, separate from time to finish the full reply.
- Delivery: Time for audio to reach the caller over the phone and network path.
Keep per-turn traces and compare the stage timings with the recorded caller-side pause. This helps distinguish, for example, a slow first audio chunk from a fast synthesis stage followed by slow delivery.
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Tune turn detection without cutting callers off
A long silence wait makes callers hear an avoidable pause. A short wait can mistake a thinking pause for the end of a turn, causing the agent to interrupt. Tune the silence duration or trigger threshold one setting at a time, then test short replies, hesitation, numbers, and interruptions.
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Check Gujarati recognition on phone audio
Clean recordings do not represent the narrow-band, noisy audio of a phone call. Test ASR on held-out 8 kHz phone speech with Gujarati-English code switching, local names, numbers, and domain vocabulary. Compare word error rate (WER) or—where a mistake on a particular name, number, or intent matters more—task-critical entity accuracy on the same calls.
IndicTelephony-Bench v1.0 describes 25,393 utterances across nine languages, totaling 30.18 hours of 8 kHz phone-line audio. Its Gujarati portion contains 2,230 utterances, 87.6% of them code-mixed. The dataset card identifies its test split as evaluation data, not training data. IndicTelephony-Bench v1.0 dataset card
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A February 2026 vendor-published benchmark from ConvoZen reports the following Gujarati results. The combined benchmark’s Gujarati subset was only 0.63 hours, so these figures are evidence about that test set, not definitive rankings for every caller or deployment. ConvoZen describes Gujarati as an evaluated language rather than a separately packaged, generally available API. ConvoZen’s Akshara ASR benchmark report
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| System in ConvoZen’s comparison | Reported Gujarati telephonic WER |
|---|---|
| Akshara | 31.41% |
| Sarvam Saaras v3 | 45.54% |
| ElevenLabs Scribe v2 | 78.89% |
The same report gives Akshara a 12.91% WER on open-domain Gujarati. Do not compare that figure directly with its telephonic result as though the audio conditions were equivalent. A wrong transcription can also add perceived delay indirectly if the system asks the caller to repeat information or takes extra turns to recover.
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Reduce model delay with focused responses
If the model is the slow stage, trim instructions and retrieved context to what the current task needs. Remove unnecessary tool descriptions, and ask for a concise, answer-first spoken response. Microsoft recommends one or two sentences per turn and expressing spoken numbers and identifiers in short, natural words. Microsoft’s response-design guidance
A speech-to-speech design can avoid some stages in a cascaded ASR-to-model-to-TTS pipeline, but it is not automatically the better choice. Prefer it when it meets your Gujarati language and control requirements. A cascaded design may be preferable when you need specific control over locale, voice, or transcription.
Measure Gujarati speech synthesis separately
For TTS, measure time to first audio chunk as well as time to finish generating the reply. Use a Gujarati-capable voice and check pronunciation, prosody, and intelligibility with callers or fluent reviewers. The cited sources establish how to monitor TTS timing, but do not establish a current, generally available Gujarati TTS choice. Microsoft’s TTS observability metrics
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Compare changes on the same calls
Change one stage or setting at a time, then repeat the same phone-call tests. When comparing approaches or providers, use the same phone path and conditions, and evaluate:
- Caller-side median and p95 pause, with the same start and end boundaries.
- Gujarati and Gujarati-English recognition accuracy on telephony audio.
- Turn-taking during hesitation, numbers, short replies, and interruptions.
- Gujarati voice availability and quality.
- Stage visibility, reliability, and cost under the same call conditions.
Track recognition quality, task completion, interruptions, and call failures alongside speed. A faster reply is not an improvement if it cuts callers off or misunderstands them. Repeat the checks after releases that affect the voice pipeline. Microsoft notes that some documented features may be in preview and not recommended for production workloads; verify the status and suitability of any feature you plan to deploy. Microsoft’s voice-agent guidance
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