Hindi and Hinglish Voice-Agent Test Kit: Names, Numbers and Real Calls

Short answer: Test the whole voice workflow using Hindi and Hinglish speech, not just written prompts. Score whether the agent preserves names, dates, amounts and caller corrections, completes authorized tasks and hands over appropriately. Record the exact model configuration and telephone path before comparing results.

The original text test kit is a starting set for staff-recorded or approved synthetic speech. It contains fictional identities and scenarios. It is not an audio dataset, an ASR benchmark or evidence of measured VaniAgent accuracy.

Start with consequential fields

Select the fields that can harm the workflow when misunderstood: appointment date, time, location, name, callback preference and amount. Use native-speaker reviewers to check that the intended meaning and wording are natural. Avoid testing only clean English translated mechanically into Hindi.

Set a fixed reference timestamp and timezone for relative-date cases. “Next Tuesday” without a reference date does not provide a reproducible expected answer. Ambiguous “four” may require a question rather than an assumed answer.

Separate recognition, reasoning and speech output

First transcribe approved recordings manually and compare recognition outputs against that reference. Then check extracted fields and the subsequent agent action. Finally, review what the caller actually hears. A correct internal value can still be pronounced unclearly or contradicted in the response.

If calculating word error rate, publish the tokenization and normalization rules, including Devanagari versus Latin spelling, numeral formatting and acceptable transliteration variants. Also report exact-match field accuracy and task completion. Do not hide an incorrect amount within a favorable average transcription score.

Introduce realistic difficulty deliberately

Run quiet audio first, then approved variants with background sound, fast speech, interruptions and code switching. Change one condition at a time so a failure can be traced. Include a caller correcting the agent, speaking over it, becoming silent or declining further interaction.

Use your actual inbound/outbound provider and codec for telephone tests. Record configuration, language settings, voice, device, sample origin and date. A browser microphone demonstration is a separate condition. Do not compare one vendor on a telephone path and another using clean uploaded audio as if the measurements were equivalent.

Score and review every failure

For each case, record transcript fidelity, consequential-field correctness, clarification quality, authorized action, audible response and handoff outcome. Mark not applicable separately from pass. Have two reviewers resolve consequential disagreements and retain sanitized evidence.

Set pass criteria before running the test. For example, an incorrect amount or false booking confirmation can be a launch-blocking task error regardless of the overall average. Your business must choose its acceptable thresholds; this guide does not claim a universal accuracy percentage.

Report results honestly

Publish the sample size, sampling method, configuration and limitations. Explain whether audio is synthetic, staff-recorded or permissioned customer speech. Consent, retention and access controls remain necessary; remove personal data before sharing examples.

Mozilla Common Voice is an example of a speech-data collection project. Its existence does not validate this kit, and dataset licenses and availability must be checked before reuse. Prefer original, permissioned workflow cases that reflect your callers. Once measured, link the results to the Hindi guide rather than claiming language superiority from untested examples.

Frequently asked questions

Does passing text prompts prove Hindi speech accuracy?

No. Text tests bypass microphones, audio codecs, recognition and synthesis. Evaluate consented or approved speech over the actual telephone path separately.

Is word error rate enough?

No. A small transcription difference can change a payment amount or date, while harmless spelling variants may not change the task. Score semantic fields and verified task outcomes as well as transcription.

Can synthetic audio replace native-speaker testing?

It can help exercise known scenarios, but it does not represent natural accents, interruptions, background noise or all users. Publish its origin and supplement it with reviewed real speech.

Continue planning your workflow

See the parent guide, calling costs, or request a scoped demonstration. Examples and test resources are educational; verified provider configuration and measured customer results must be evaluated separately.

Practical worksheets and implementation guides

• Hindi and Hinglish Voice-Agent Task Accuracy: A Transparent Evaluation Method

For carrier formats, reference transcripts and recognizer comparisons, read testing Hindi speech recognition on real telephone audio.

For capture, transport and recovery diagnosis, read Hindi voice agents on noisy calls.