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

Evaluate Hindi and Hinglish voice agents at three levels: speech recognition, consequential field accuracy and completed business tasks. A transcript can differ from a reference while preserving the intended task, or look fluent while saving the wrong date. Report those outcomes separately.

Use the evaluation worksheet alongside the existing small test kit. This is a scoring protocol, not a benchmark result or claim of native-speaker validation already completed.

Build a permissioned reference set

Use consent-safe audio with representative tasks, speakers and recording conditions. Record speech origin: synthetic text, synthesized speech, studio speech or actual authorized handset audio. These inputs test different boundaries and should not be silently combined.

Have a competent reviewer produce the reference transcript and intended final fields. Include corrections, code switching, names, dates, amounts, interruptions and human requests. Keep the original audio and reference version so later scoring changes are traceable. Avoid publishing personal or sensitive customer data.

Normalize without hiding the task

Decide whether equivalent Devanagari and transliterated forms count as the same word, how numbers are represented and how punctuation is treated. Apply the same rules to reference and hypothesis. Preserve the unnormalized originals.

Google Cloud documents word error rate using substitutions, deletions and insertions against reference words. Use that speech metric only for the declared transcription task. It does not prove a booking was correct or that the final corrected amount was understood.

Score consequential fields independently

Mark ambiguity explicitly. If AM/PM or reference date is missing, clarification may be the correct outcome. Do not award a guessed date a pass because it happens to match the evaluator's assumption.

Review tasks and disagreements

Inspect saved event/task identifiers, duplicate writes, authorization and spoken confirmation. Score false success claims as failures even when the conversation sounds natural. Separate attempted, completed, blocked and safe-fallback outcomes.

Have independent reviewers assess a defined subset and record disagreement by category. Adjudicate with the audio and policy, preserve the initial decisions and explain the final rule. Do not remove difficult accents or ambiguous clips simply to improve a headline percentage.

Report sample counts, task mix, configuration, speech origin, normalization and exclusions. Use per-task and per-condition results rather than one universal accuracy claim. The method can be published now; credible language-performance claims require the actual native-reviewed evidence.

Frequently asked questions

Is word error rate enough for Hindi voice agents?

No. It measures transcription differences, while entity accuracy, decisions and saved outcomes require separate task-level checks.

Can transliterated Hindi and Devanagari be compared directly?

Choose and disclose normalization rules before scoring. Preserve originals and avoid normalization that hides consequential errors.

Does this article claim a measured accuracy percentage?

No. It defines a method. Results require permitted audio, native-speaker review and authoritative outcome evidence.

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.