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Voice evaluation · Hinglish

Hinglish AI Voice Evaluation

Human evaluation of your speech models: MOS and preference testing for TTS, WER-in-context review for ASR, and native-speaker judgement on naturalness and intelligibility. This page covers how that works specifically for Hinglish, where intra-sentential switching means english words carry indian phonology, so english acoustic models mis-transcribe them.

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Evaluator scoring AI voice output against a rubric — Hinglish AI Voice Evaluation
Language
Hinglish (hi-Latn-IN)
Dialects covered
4
Typical programme
500-2,000 hours
Cities
Delhi, Gurugram, Noida
01

What changes when the language is Hinglish

The service specification stays constant across languages; the linguistics do not. For Hinglish, three things drive the design of a voice evaluation programme.

  • Intra-sentential switching means English words carry Indian phonology, so English acoustic models mis-transcribe them
  • Dialect spread: Delhi corporate Hinglish, Mumbai Bambaiya, Call-centre register, Youth/social media register
  • Hinglish is the code-mixing case itself. Typical urban customer-support speech is 30-60% English tokens embedded in Hindi grammar, with switching several times per utterance.
Voice evaluation — Hinglish · written into the SOW before recordingTTSMOS (1-5), MUSHRA, and A/B preference protocolsASRError typing: substitution, deletion, insertion, code-switc…PanelNative speakers of the target variety, screened and calibra…Sample sizePowered per the effect size you need to detectReportingPer-item scores plus aggregate with confidence intervalsYour values replace ours rather than being converted after delivery.
02

Technical specification

ParameterStandard
TTSMOS (1-5), MUSHRA, and A/B preference protocols
ASRError typing: substitution, deletion, insertion, code-switch failure
PanelNative speakers of the target variety, screened and calibrated
Sample sizePowered per the effect size you need to detect
ReportingPer-item scores plus aggregate with confidence intervals
Data visualisation of studio and field recording coverage across India — supporting hinglish ai voice evaluation
Data visualisation of studio and field recording coverage across India
03

Hinglish cohort design

Recruit by switching behaviour, not by language proficiency. Screening recordings are used to confirm speakers switch naturally rather than performing one language.

DimensionTypical splitWhy it matters for Hinglish
Gender50 / 50Pitch range differences change acoustic model behaviour; unbalanced cohorts bias recognition
Age18-25: 30%, 26-40: 40%, 41-60: 30%Older speakers retain conservative Hinglish forms that younger urban speakers have lost
RegionDelhi NCR / Mumbai / Bengaluru and othersDialect spread across 4 recognised varieties
EducationMixed, including below-graduatePrompt-reading fluency correlates with education and skews prosody
ConditionStudio / quiet room / fieldMatch the noise profile of your deployment
04

Process

  • Protocol design and sample-size calculation
  • Panel recruitment and calibration on reference items
  • Blind evaluation with attention checks
  • Statistical analysis
  • Report with per-error-type breakdown
05

Hinglish-specific quality rules

  • Whether English tokens are written in Latin or transliterated into Devanagari must be fixed by rule, not left to annotators
  • Language-ID tagging per token is required for training but is skipped by most vendors
  • Ambiguous words shared by both languages need an explicit tie-break rule

Attention checks and reference anchors are embedded so unreliable raters are detected and excluded before analysis.

06

Deliverables

  • Raw per-rater scores
  • Aggregated results with confidence intervals
  • Error-type analysis
  • Recommended fix priorities
07

Worked example

A representative Hinglish voice evaluation engagement: 500 hours from 1,000 speakers, 50/50 gender, ages 18-45, spread across Delhi, Gurugram, Noida, recorded to the specification above and delivered in WAV with a per-utterance manifest.

Timeline: 1-3 weeks per evaluation round.

08

Where this data is missing today

Almost no public corpus contains genuine intra-sentential Hindi-English switching with per-token language tags. This is the highest-value gap for anyone building Indian conversational AI.

Frequently asked

How much does Hinglish voice evaluation cost?

Priced per delivered hour or unit against a written spec. The cost drivers for Hinglish are dialect spread, demographic narrowness and recording condition, in that order.

Which Hinglish dialects are included?

By default Delhi corporate Hinglish, Mumbai Bambaiya, Call-centre register, Youth/social media register and others, tagged per speaker. You can also commission a single-dialect corpus if you are targeting one region.

Can you deliver Hinglish data in our format?

Yes. Raw per-rater scores is the default, but naming, schema and directory structure follow your pipeline.

How long does a Hinglish programme take?

1-3 weeks per evaluation round.

Request a Hinglish voice evaluation quote

Hours, speakers, dialects, deadline. Send what you have.

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