Hinglish call QA needs more than English WER
The short answer
Indian BPO floors do not speak “English with an accent.” Agents code-switch mid-sentence: Hindi morphology, English product names, Latin digits, Devanagari verbs. If your QA stack assumes clean English ASR and a 2% sample, you will miss compliance and coaching failures on the exact calls that matter. [SRC001] [SRC004]
Score Hinglish with mixed-script evaluation, entity checks, and a census—every call—not a handful of English recordings. [SRC002] [SRC003]
Code-switch is the product, not an edge case
CoSHE-500 exists because conversational Hindi–English switching is where most Hindi/English models degrade. References keep Devanagari for Hindi and Latin for English on purpose. The dataset maintainers warn that Whisper's default text normaliser strips Devanagari matras and inflates WER—so even your scoring recipe can lie. [SRC001]
If your vendor only shows an English WER dashboard, you are not measuring the floor you hired.
WER is necessary. It is not sufficient.
AssemblyAI's 2026 critique is blunt: classic WER treats every token equally. For BPO QA, a wrong account number or amount is not equal to a dropped filler. Voice and contact-center teams need semantic/entity metrics beside WER. [SRC002]
On Hinglish, add another failure mode: loanwords and digits that stay in Latin while the reference is forced into Devanagari (or the reverse). State the output convention before you trust a vendor leaderboard. [SRC001]
Sampling makes language bias invisible
Manual programs still review on the order of 2% of calls. That sample is usually whoever was easy to pull—not a balanced draw across Hindi-heavy, English-heavy, and mixed campaigns. Fatal disclosure misses hide in the other 98%. [SRC004]
We covered the sampling math in why 2% call QA sampling fails Indian BPOs. Language mix makes the same math worse.
What a Hinglish-ready QA stack does
- Transcribe with mixed-script output, not forced monolingual transliteration. [SRC001]
- Score entities and rubric items (openings, compliance, objections, empathy, fatal errors) on every call. [SRC003]
- Use humans on fails and outliers, not on random “average” English calls. [SRC004]
- Keep one rubric across human agents and AI voice so drift is comparable.
CallPulse is Qualia's product answer for that operating model: 100% review with Hindi, English, and Hinglish support on BPO floors. [SRC003]
Ship checklist
- Ask every ASR/QA vendor for Hinglish code-switch numbers, normaliser details, and entity error rates—not only English WER. [SRC001] [SRC002]
- Build a held-out set of your own campaign audio (do not train on public eval clips).
- Turn on 100% rubric scoring; stop pretending a 2% English sample represents the floor. [SRC004] [SRC003]
FAQ
Why does English WER fail on Hinglish calls?
Hinglish mixes Devanagari Hindi and Latin English inside one utterance. Benchmarks like CoSHE-500 exist because general Hindi/English models degrade on that code-switch. An English-only normaliser also mangles Devanagari scoring.
Is WER enough for BPO QA?
No. WER treats every word equally and can miss the entity errors that matter for compliance—names, amounts, IDs. Pair ASR metrics with rubric scoring across every call.
Does Qualia support Hinglish QA?
CallPulse documents Hindi, English, and Hinglish transcription and QA scoring for BPO floors, with 100% call review instead of a tiny sample.
Sources
- CoSHE-500 — Conversational Hinglish Speech Evaluation — Hugging Face / Trelis (from Soket Labs CoSHE)
- Word error rate is broken: How to actually evaluate speech-to-text in 2026 — AssemblyAI
- CallPulse — AI Call Auditing for BPOs — qualiabits.com
- From 2% to 100%: what changes when every call gets reviewed — Krisp
Evidence map
- Conversational Hinglish is a distinct ASR failure mode; dedicated code-switch eval sets exist because Hindi/English monolingual models degrade there.
Evidence: SRC001 - WER alone is a weak production QA metric because it ignores semantic and entity importance.
Evidence: SRC002 - Manual sampling still leaves most calls unscored, which is worse when language mix varies by agent and campaign.
Evidence: SRC004 - CallPulse is positioned for Hindi, English, and Hinglish floors with 100% call scoring.
Evidence: SRC003