A Gujarati phone agent feels slow when callers wait too long after finishing a turn—not merely when a server takes time to start generating text. Measure the pause from the end of the caller’s speech to the first audible agent response, then isolate endpointing, speech recognition, model generation, text-to-speech, and phone-network delivery. Tune the slow stage using real 8 kHz phone audio, including Gujarati-English code switching, and check that faster replies still understand callers and respect their pauses.
Measure the pause the caller actually hears
For each turn, measure from the end of the caller’s speech to the first audible agent audio. Server-side time-to-first-byte is useful for diagnosis, but it may omit endpointing and network delivery—the parts of the delay that the caller also experiences.
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OpenBenchmarks reports a lowest median caller-side latency of 1,296 ms among five platforms in its 2026 comparison, based on recorded real phone calls. That is a general platform comparison, not a Gujarati benchmark or a universal target. The page also cautions that results with different measurement boundaries should not be compared directly. Use the figure as context, not as a pass/fail threshold for a Gujarati agent. OpenBenchmarks’ 2026 comparison
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Set a baseline on your actual phone route. Keep the line, caller script, and turn conditions consistent, and record both the median and slower-tail results. A change that improves the median but leaves occasional long pauses untouched may not fix the calls users find most frustrating.
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Trace every stage before changing settings
A voice agent’s response time is a pipeline, so instrument each turn rather than guessing which component is responsible. Microsoft Foundry’s guidance recommends monitoring time to first audio and stage timings. Its observability documentation distinguishes model token timing, ASR latency and real-time factor, and TTS first- and last-audio timing. Microsoft’s voice-agent best practices and Microsoft’s observability documentation
- Endpointing and turn detection: How long the system waits before deciding the caller has finished.
- ASR: How long speech recognition takes, and whether errors trigger clarification or repetition.
- Model: Time until the model begins producing a response, including any tool or retrieval work.
- TTS: Time until the first audio chunk, as distinct from time until the full response is synthesized.
- Phone and network transport: Time for audio to reach the caller after the agent starts speaking.
Keep a trace for each turn and compare stage timings alongside caller-side pause measurements. If the pipeline looks fast in logs but the caller still waits, check whether your internal clock starts too late or stops before audio reaches the phone.
Adjust turn detection without interrupting callers
A long silence wait makes a finished caller sound slow; an overly short wait can cut off someone who pauses mid-thought. Microsoft advises tuning silence duration to caller behavior and considering semantic turn detection when people pause while speaking. It specifically notes that callers who think aloud, read numbers, or are not speaking their first language may need a longer silence duration. Gujarati callers may show any of these patterns, so validate on real calls rather than assuming an English-oriented default fits.
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- Review turns where the pause occurs before the system accepts the caller’s turn. Confirm that endpointing—not recognition or model processing—is the delay.
- Reduce the silence wait or adjust the trigger threshold cautiously, one setting at a time.
- Test short answers, hesitation, mid-sentence pauses, spoken numbers, and interruptions with Gujarati callers.
- Check whether callers are cut off or have to repeat themselves before keeping the change.
Microsoft recommends testing short answers, hesitation, and interruption cases. A lower silence threshold is not an improvement if it creates more repair turns or makes callers feel rushed. Microsoft Foundry turn-detection guidance
Test Gujarati recognition on phone audio and code switching
Clean recordings do not represent every phone call. Evaluate recognition on 8 kHz telephony audio, with Gujarati-English code switching, local names, numbers, and the vocabulary your callers use. Compare systems on the same held-out calls using word error rate (WER) or, where it matters more, accuracy for task-critical details such as names and numbers.
IndicTelephony-Bench v1.0 contains 25,393 utterances and 30.18 hours of 8 kHz phone-line audio across nine languages. Its Gujarati portion has 2,230 utterances, and the dataset card reports that 87.6% are code-mixed. The card identifies the test split as evaluation data, not training data. IndicTelephony-Bench v1.0 dataset card
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A February 2026 ConvoZen-published benchmark reports these Gujarati results:
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The report’s Akshara result on Gujarati open-domain audio is 12.91% WER, a different condition from telephonic speech. Its Gujarati telephony comparison used a 0.63-hour Gujarati subset of the combined benchmark, so these vendor-published results are not definitive universal rankings. ConvoZen describes Gujarati as an evaluated language rather than a separately packaged, generally available API. Treat the figures as a reason to test your own calls, not as proof that one recognizer will perform best for your users. ConvoZen’s Akshara ASR Benchmark Report
Use only data you are authorized to evaluate, and keep evaluation calls separate from training data. A recognizer that gets a word wrong can add more delay than its own processing time if the agent then asks for repetition or acts on an incorrect number.
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Help the model start speaking sooner
If model first-token timing is the slow stage, remove work that does not improve the spoken answer. Microsoft recommends keeping prompts and retrieved context focused, reducing unnecessary tool descriptions, and using a short answer-first style. For voice turns, it suggests one or two sentences and rendering numbers or identifiers in short, natural spoken forms.
- Keep system instructions concise and specific to the current task.
- Retrieve only context needed for the caller’s question rather than passing broad documents.
- Limit tool selection to tools the current task can actually require.
- Design the first spoken sentence to answer or acknowledge the caller before adding detail.
Where language support and control requirements permit, a speech-to-speech design can reduce stages. A cascaded ASR-to-model-to-TTS design may still be preferable when you need more control over locale, voice, or transcription. Choose based on measured end-to-end behavior and task quality, not architecture alone. Microsoft’s guidance on voice-agent design
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Measure synthesis and transport separately
If the model responds quickly but the caller hears a delay, separate TTS time to first audio from the time to synthesize the full answer. Then check phone and network delivery. Optimizing total synthesis time will not necessarily make the first audible response arrive sooner if the system waits for a complete waveform before playback.
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Use a Gujarati-capable voice and listen for pronunciation, prosody, and intelligibility on the same kinds of calls your agent handles. The sources available here do not establish a current, generally available Gujarati TTS choice, so verify language availability and voice behavior for the specific service and configuration you plan to deploy. Microsoft’s observability documentation describes first- and last-audio metrics for diagnosing synthesis timing. Microsoft Foundry observability guidance
Compare options on the same calls
Neither a general voice-platform latency comparison nor a small vendor-published Gujarati ASR comparison settles which platform is right for a Gujarati phone agent. Compare alternatives under the same call conditions and include both speed and caller experience:
- Caller-side median and p95 pause on the same phone path.
- Gujarati and Gujarati-English recognition accuracy on held-out telephony calls.
- Turn-taking on hesitation, numbers, short replies, and interruptions.
- Gujarati voice availability, pronunciation, prosody, and intelligibility.
- Visibility into stage timings, reliability, and cost under the same call conditions.
Retest after releases and inspect slow-tail turns as well as the median. Also check recognition quality, interruption timing, task completion, and call failures: a faster agent that misunderstands callers or cuts them off has not delivered a better experience. Microsoft’s evaluation and monitoring guidance, ConvoZen’s benchmark limitations, IndicTelephony-Bench’s evaluation data, and OpenBenchmarks’ latency methodology
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