Handling Hindi, Hinglish, and Dialect Variation in Spoken Business Data
Table of Contents
The Real World Speaks Hinglish and Regional Dialects
Standard commercial speech recognition models are trained on formal news broadcasts or polished audiobooks. In contrast, daily commerce across Indian MSMEs and agricultural markets operates in code-switched Hindi, Hinglish, and regional vocabulary.
A factory supervisor dictating stock movements does not say: "Please register a receipt of 500 kilograms of structural steel." They say: "Bhai, 500 kilo saria aaya hai, invoice number 4022, party Sharma Traders."
Building voice software for Indian business operations requires models that fluidly parse mixed English numbers, regional commodity names, and local units of measurement without forcing users to adopt artificial, robotic phrasing.
Constraining Speech via RAG Catalog-Matching
If you rely solely on open-ended Speech-to-Text (STT) models, local words like "Saria" (Rebar), "Gaddi" (Bundle), or regional unit terms like "Katta" (Sack) often transcribe into phonetically similar English words, destroying database accuracy.
We solve this through Retrieval-Augmented Generation (RAG) catalog constraint:
1. Context Injection: When a user speaks, the audio processor receives the tenant’s actual ERP master data—their exact customer list, inventory catalog, and unit definitions.
2. Phonetic Vector Search: If the transcribed text yields "Sariya 50 Katta", the system runs a vector similarity search against the business’s registered SKU catalog (e.g., SKU-104: 12mm TMT Rebar, Unit: 50kg Bags).
3. Entity Normalization: The raw spoken input is mapped directly to canonical database entries, ensuring clean, structured SQL inserts regardless of how colloquially the item was spoken.
Current Technical Boundaries and Known Limitations
We believe in total engineering honesty regarding what our speech pipelines can and cannot do today:
• High Acoustic Noise: Heavy press machines operating at >85 dB require directional noise-cancelling microphones or close-range smartphone input; ambient smartphone mics in extreme noise will experience dropped syllables.
• Unregistered SKUs: If a user dictates a brand-new raw material item that has never been entered into their ERP catalog, RAG matching cannot guess the correct item code. The system triggers an "Unmapped Item" prompt during the Review step.
• Complex Multi-clause Sentences: Long, run-on sentences containing multiple unrelated actions ("Dispatch 10 boxes to Ramesh and also check why machine 3 is leaking oil") can confuse intent classification. We train operators to dictate one discrete business transaction per voice clip.
Article FAQs
Does the voice engine require an active internet connection to transcribe speech?↓
For complex Hinglish RAG parsing, cloud connectivity delivers peak accuracy. However, our mobile clients include lightweight on-device acoustic models capable of capturing audio offline and queuing transcription until connectivity is restored.
Which languages are supported out of the box?↓
We natively support conversational English, Hindi, Hinglish, and regional vocabulary common across Rajasthan and North/Central India. We are expanding regional language models iteratively based on active deployment needs.