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Language is where most voice AI systems fall apart in the Indian market. A model trained on clean, studio-quality recordings of standard Hindi does not hold up against a customer calling from a tier-3 city on a 2G connection, mixing Rajasthani-inflected Hindi with English product names. VInfer built Neuron’s language engine from the ground up for India’s actual linguistic reality — not a translation layer on top of a Western model, but a voice-native system trained on the diversity, noise, and code-switching patterns of real Indian customer calls.

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Supported Indian Languages

Neuron covers India’s major scheduled languages plus regionally spoken variants that are critical for high-volume calling in specific geographies:

Hindi (hi-IN)

Standard Hindi with coverage of Hindustani variants. Handles Devanagari-script literacy levels, formal and informal registers, and heavy English code-switching.

Tamil (ta-IN)

Tamil with dialect sensitivity across Tamil Nadu and Sri Lankan Tamil communities. Handles both formal literary Tamil and spoken colloquial forms.

Telugu (te-IN)

Telugu with coverage of Andhra Pradesh and Telangana dialectal variation — distinct enough in practice to matter for agent performance.

Marathi (mr-IN)

Marathi including regional variations across Vidarbha, Marathwada, and coastal Maharashtra. Handles English and Hindi borrowings common in urban Marathi speech.

Kannada (kn-IN)

Kannada across its northern and southern dialects, with robustness for Bangalore’s heavily code-switched urban variety.

Bengali (bn-IN)

Bengali with coverage for both West Bengal and Bangladesh-origin speakers. Handles the significant phonological differences between Kolkata and Dhaka varieties.

Gujarati (gu-IN)

Gujarati with dialect sensitivity across Saurashtra, Kutchi, and urban Ahmedabad varieties. Strong English code-switching support for business communities.

Punjabi (pa-IN)

Punjabi (Gurmukhi) including coverage for Malwi, Doabi, and Majhi dialectal regions. Also handles Punjabi-Hindi-English trilingual code-switching common in urban callers.

Malayalam (ml-IN)

Malayalam with sensitivity to the strong regional dialectal variation across central and northern Kerala. Handles English-heavy professional speech common among Kerala’s large educated workforce.

Odia (or-IN)

Odia with coverage for standard and coastal varieties. Handles Hindi and English borrowings common in urban Bhubaneswar and Cuttack speech.

Bhojpuri

Bhojpuri spoken across eastern Uttar Pradesh and Bihar. Critical for rural financial services and FMCG outreach at scale in this belt.

Rajasthani & Haryanvi

Regional spoken variants of western Hindi — frequently used for rural collections and government scheme outreach in Rajasthan and Haryana.
Additional Indian language and dialect variants are added regularly. Contact your VInfer account team for the current full list or to request prioritization of a language your campaigns require.

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Supported English Variants

Indian English (en-IN)

The primary English variant for India-based campaigns. Trained on Indian-accented speech across major metro and tier-2 cities. Handles Indian phonological patterns, rhythm, and intonation that differ significantly from Western English models.

British English (en-GB)

Standard British English for UK-facing campaigns or Indian customers with British English exposure.

US English (en-US)

Standard American English for North American campaigns or global customer segments.
Use en-IN for any English-language campaign targeting customers in India. Neuron’s en-IN model significantly outperforms en-US for Indian-accented speech — connect rates and comprehension scores are consistently higher. Reserve en-US for campaigns explicitly targeting a North American customer base.

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Language Code Reference

Use these BCP-47 codes when setting the language parameter in your agent configuration or campaign API calls:
LanguageRegion/VariantBCP-47 Code
HindiIndiahi-IN
TamilIndiata-IN
TeluguIndiate-IN
MarathiIndiamr-IN
KannadaIndiakn-IN
BengaliIndiabn-IN
GujaratiIndiagu-IN
PunjabiIndiapa-IN
MalayalamIndiaml-IN
OdiaIndiaor-IN
EnglishIndiaen-IN
EnglishUnited Statesen-US
EnglishUnited Kingdomen-GB
Bhojpuri, Rajasthani, and Haryanvi are supported as acoustic models under the hi-IN language family. Contact your account team for the specific model variant identifiers for these languages.

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Code-Switching

Code-switching — switching between two languages mid-sentence — is the norm for hundreds of millions of Indian speakers, not the exception. A customer might say: “Bhai, mujhe is EMI ke baare mein koi issue nahi hai, but the amount looks wrong on my statement.” Neuron handles this natively. It does not require the customer to stay in one language, and it does not lose conversational context when the language shifts. Specifically:
  • Mid-sentence switches are processed as a unified utterance — Neuron understands the complete meaning across both languages
  • Domain vocabulary in English (loan amount, EMI, statement, OTP, PIN) is recognized correctly even when embedded in a non-English sentence
  • Asymmetric switching — where the customer speaks primarily in one language but Neuron responds in another based on your agent configuration — is fully supported
  • Script-to-language mismatch handling — if a customer responds in a language other than the agent’s primary language, Neuron detects this and adapts rather than failing
Customer: “Mera loan ka due date kab hai? And can I pay online?”Neuron processes: Full meaning across both segments — due date query (Hindi) + payment channel query (English) — and responds to both in a single, natural turn.

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Acoustic Robustness

Indian call center audio presents challenges that break most voice AI systems. Neuron is specifically engineered to handle them:
Neuron maintains high comprehension accuracy on 8kHz telephony audio — the standard for PSTN and many mobile networks in India — without requiring HD audio.
Training includes real-world call center recordings with typical background conditions: street noise, household sounds, overlapping conversations, and poor acoustic environments common in tier-2 and tier-3 locations.
Each language model is trained on regionally diverse speaker pools. A hi-IN call from Bihar is not processed the same way as one from Delhi — the model accounts for phonological variation across geographies.
Neuron handles fast talkers, slow deliberate speakers, and the mid-call pace changes that happen when customers switch from casual to serious mode (or vice versa).
Echo, clipping, packet loss, and DTMF tones in the audio stream are handled gracefully — Neuron does not misinterpret these as speech or lose its place in the conversation.

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Setting Language in Your Campaign

Set the language parameter when creating a campaign via the API: 
curl -X POST https://api.vinfer.ai/v1/campaigns \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Tamil Nadu Collections — June",
    "script_id": "script_collections_ta_v1",
    "language": "ta-IN",
    "contacts": [...]
  }'
Or select the language in the dashboard when creating a new campaign under Neuron → Campaigns → New Campaign → Language.
The language set on the campaign overrides the default language on the voice agent for that campaign run. Make sure your script content matches the language you configure — running a Hindi script with ta-IN set will produce degraded results.

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Next Steps