🗣️ Alexa, show how I improved your हिंदी by 18% for Indian users
Never thought I'd be teaching a voice AI the nuances of "Hinglish." But there I was, part of Amazon's Alexa team in Bengaluru, trying to explain why "light band kar do"
was perfectly valid Hindi, even though no Hindi textbook would agree.
NLP Engineer, Alexa India
Gearing up Hindi language model improvements
Team
10+ people across NLP, QA, and Alexa Experience Teams
Timelines
5 months, 2022
Starting Point
It was a regular Tuesday morning standup when our PM dropped what seemed like a simple request — "We need to make Alexa's Hindi more natural."
Simple, right? Well, about that...
A Quick Context
When this story begins, Alexa already "spoke" Hindi
. But there's a difference between speaking a
language and understanding its soul. Imagine a foreigner who learned Hindi from textbooks trying
to chat with your grandmother - that was Alexa in 2022.
Breaking Down the Problem
Frustrated sigh 😮💨
In a test session, a user tried 3 different ways to ask "current weather in thier location" - each more
natural than the last. Alexa understood the most formal version but missed the ones people actually used at home
.
First, let me show you what we were dealing with. Imagine trying to map every possible way someone might ask Alexa for weather outside:
The challenge wasn't technical at first - it was cultural
.
The issue isn't that Alexa can't understand Hindi. It's that she understands textbook Hindi when no one in India does speak.
Rahul, Linguistics Lead
Our research approach had to be different - we couldn't just rely on data and analytics we see in the console. We had to understand the cultural context
of how Indians spoke Hindi. So, we did two things:
- In-person/Virtual Home Visits: Through in-person vists and video calls, we observed 200+ families interact with Alexa in their natural environment.
- Dialect Mapping: Created a comprehensive map of how Hindi changes across regions.
Picture a typical Indian household
Reality of Indian conversations - Ethnographic Interviews
It's dinner time. The TV is playing a Hindi serial. Mom asks in Hinglish, "Beta, volume thoda down kar do." Dad responds in pure Hindi. The kids mix three languages in one sentence. This is the natural flow of Indian conversation - fluid
, mixed
, and contextual
.
Dialect Wall
Remember that scene in detective movies where they have a wall covered in photos connected by red string? We built something similar, but for language. We called it the Dialect Wall - a massive map of India with words
connecting different regions, each string representing how the same phrase changed as you moved across the country.
The moment we stopped thinking of Hinglish as broken Hindi and started seeing it as its own language, everything clicked. Solution wasn't in the code - it was in the culture.
Team's NLP Lead
BERT Breakthrough
Here's where it gets slightly technical. We were banging our heads against the wall trying to create rules for every possible variation when someone said, "What if we let BERT figure it out?"
BERT (Bidirectional Encoder Representations from Transformers)
was like that friend who grew up in a multilingual household - naturally switching between
languages without thinking about it. We just had to feed it enough examples.
We collected the most common phrases from CLEO Skill and fed them to BERT. The results were... well, let's just say we were glad we didn't have to write another rule.
Breakthrough Approach
Remember when we used to think mixing languages was a problem to solve? Turns out, it was the solution. Here's where it gets interesting (and where I have to be careful with NDA details).
Here's what we did:
-
Started from Scratch
- Threw out the traditional language model approach
- Built a new model that didn't try to separate languages
- Trained it on real conversations, not textbook Hindi
-
The Multi-Dialect Dataset
- Collected natural conversations from 200+ households
- Mapped common patterns across regions
- Adapted a Multi-lingual "Natural Speech" corpus
Finding #127:
Commands using mixed languages had a 23% higher success rate
when processed through our unified
model compared to traditional language-switching approaches.
Testing Phase
This is where things got interesting. We set up what we called "The Living Room Test"
- virtual sessions where we watched how families naturally interacted with Alexa.
Reality Check:
During testing, we found that most users didn't even realize they were mixing languages. That's when we knew we were on the right track.
Before
Rigid, textbook Hindi responses that felt unnatural and formal
After
Dynamic responses that matched user's natural speaking pattern and dialect
Results That Mattered
Happy to see after months of teaching Alexa to think in Hindi (and Hinglish, and everything in between), even after my transition to the US, the numbers from public reports told an interesting story. While I had moved on to pursue opportunities in the United States, it was gratifying to learn through Amazon's public announcements about the growing impact of Hindi language understanding on Alexa's user base:
Uses approved public metrics while
maintaining NDA compliance
.
But the real victory? When a grandmother in Lucknow told us,
Pehli baar lagta hai ki machine nahi, koi apna bol raha hai. (For the first time, it feels like I'm talking to someone who knows me, not a machine.)
68 year old Grandma, from Lucknow, during final testing
Key Takeaways
-
Start With People, Not Data
- Understanding how people naturally speak is more important than linguistic correctness
- Cultural context matters more than grammatical accuracy
-
Think in Patterns, Not Rules
- Language is fluid, especially in India
- Let the model learn patterns rather than forcing rules
-
Test in Real Homes
- Lab testing can't replicate real Indian household chaos
- Background TV noise, multiple speakers, mixed languages - it all matters
Just say "Alexa, Hindi mei baat karo" and experience it yourself ✨
Want to dive deeper into how I voiced out for contextutal research mixed-language queries?
Get in touch to schedule a presentation.