Multilingual Translation Device
Omkar Jadhav (Member) Information Technology SVCP, Pune
Shrushti Nikam (Member) Information Technology SVCP,
Pune Raghav Gunda (Member) Information Technology SVCP,
Pune Ms. S. U. Kavale (Guide)
Abstract: The Multilingual Translation Device is Text-to-Text, Text-to-Speech,
designed to provide seamless real-time language
translation for multiple Indian languages. Our project
integrates speech recognition, natural language
processing, and audio synthesis to enable speech-to-
speech, speech-to-text, and text-to-text translations.
Built with a compact embedded system and powered
by a Java-based mobile application, the device allows
users to communicate effortlessly across language
barriers. This system emphasizes offline translation
capabilities, regional language support, and cost-
effective deployment. The project demonstrates
potential in public service sectors, tourism, and
healthcare to promote inclusive communication.
Keywords
Language Translation, Speech Recognition, Indian
Languages, NLP, Java Application, Offline
Communication
A. Introduction
In today's globalized and digitally connected world,
the ability to communicate across language
boundaries has become increasingly vital. This is
particularly relevant in multilingual nations like India,
where linguistic diversity is both rich and widespread.
With 22 officially recognized languages and hundreds
of dialects, India presents a unique challenge:
enabling seamless communication among individuals
who do not share a common language. Language
barriers in such settings can hinder access to essential
services, limit educational and economic
opportunities, and create significant social divides.
To address these challenges, this paper introduces the
design and development of a Multilingual Translation
Device, an intelligent system engineered to facilitate
real-time communication between speakers of
different Indian languages. Unlike many existing
translation tools that rely heavily on constant internet
connectivity and offer limited support for regional
languages, this device is designed to function
efficiently in both online and offline environments. It
supports multiple modes of translation including
Page 1 of 6
and Speech-to-Speech, allowing users to choose the
most appropriate form of interaction based on their
context and preferences. Developed using Java and
integrated with a mobile application, the system
offers a user-friendly interface and is optimized for
low-resource settings. The primary focus of the
device is on accuracy, contextual relevance, and
speed, ensuring that the translated output maintains
the intended meaning and tone of the original
communication. It also prioritizes ease of use,
making it accessible for people from different
educational backgrounds, including those who may
not be tech-savvy.The proposed system has vast
applications in sectors such as healthcare, education,
travel, public administration, and customer service.
By enabling cross-lingual conversations, the device
has the potential to enhance social inclusion, improve
public service delivery, and empower individuals in
both rural and urban areas to participate more
actively in their communities and economies.
B. Literature Survey
Over the years, various translation technologies have
emerged, ranging from rule-based models to
advanced neural machine translation (NMT) systems
such as Google Translate and Microsoft Translator.
These platforms leverage deep learning and large
datasets to deliver accurate translations but often
rely on continuous internet access and provide
limited support for regional Indian languages.
Government-led initiatives like Bhashini and TDIL
have contributed valuable linguistic resources, yet
their integration into real-time, offline-capable
devices remains limited. Recent advancements in
speech recognition, attention mechanisms, and
transformer-based architectures have enhanced
translation accuracy, but there is still a gap in
solutions that combine multimodal translation—
text-to- text, text-to-speech, and speech-to-speech—
into a single, user-friendly device tailored for India’s
diverse linguistic landscape.
C. Background
Page 2 of 6
E. Hardware Specification
India's linguistic diversity creates communication
barriers, especially in rural areas with limited access to 1. Microcontroller: ESP32 – a low-cost Wi-Fi-enabled
translation services. Existing solutions, like Google microcontroller for processing and handling user inputs.
Translate, often require internet connectivity and fail to
support regional languages effectively. Although 2. Audio Amplifier_ PAM8403.
government initiatives like Bhashini aim to improve
language technology, there is a lack of real-time, 3. Audio Interface:
offline devices that support multiple input and output Microphone: High-sensitivity omnidirectional
formats. This highlights the need for an accessible, microphone for speech input.
portable multilingual translation device for seamless Speaker: 3W mini speaker for audio output in translated
communication across India's diverse linguistic languages.
landscape.
4. Power Supply: 3.7V 2000mAh Li-Ion rechargeable
battery, supporting several hours of operation.
D. Process Table
5. Connectivity:
Step Action Description Micro USB port for charging and programming.
No. Wi-Fi (via ESP8266) for data syncing with the mobile
app.
1 Voice Input Capture voice input 6. Optional: 3.5mm audio jack for earphone-based audio
Capture via microphone and output.
send to processor
F. Methodology
1. Research & Planning: Reviewed Indian language
datasets and existing translation models.
2 Speech Recognition Convert input speech 2. Component Selection: Chose ESP32, microphone,
to text using offline speaker, and rechargeable battery for portability
speech-to-text engine. 3. Mobile Application: Built in Java to provide UI,
select languages, and show logs.
4. Firmware Development: Programmed ESP32 to run
speech recognition and translation modules.
5.
Translate
Testing & Optimization: Conducted real-lifetext from
conversations to tune accuracy, latency, and usability.
3 Text Translation source to target
language using
translation engine G. Implementation
The multilingual translation device is implemented using
an ESP8266 microcontroller, which handles processing
4 Voice Output Repeat the control tasks and connects to other components, including a 2.4-
inch TFT display, microphone, 3W speaker, and a 16GB
loop steps until MicroSD card for offline storage. The firmware is
the mission developed in C/C++ to control speech recognition,
objectives translation, and output. Pre-trained neural machine
translation (NMT) models are used for text translation,
are achieved.
while speech-to-text and text-to-speech models handle
audio input and output. The device also communicates
5 Mobile Sync Display translations on with a Java-based mobile application, allowing users to
connected mobile app update translation models and sync data via Wi-Fi.
for user convenience. Extensive testing is conducted to optimize translation
accuracy, reduce latency, and ensure seamless
performance in real-world use
Page 3 of 6
H. Features M. Acknowledgement
1. Multilingual Support: Supports multiple Indian We want to thank our Head of Department Mr. U. S.
languages for text and speech translation. Shirshetti whose guidance and support made this
2. Offline Functionality: Operates without internet project successful. We also want to thank our faculty
connectivity using pre-loaded language models. members and colleagues of Information Technology
3. Text-to-Text & Speech-to-Speech Translation: Dept. who supported us in developing this project.
Enables both text and voice-based translation.
4. Text-to-Speech: Reads translated text aloud.
N. References
5. Portable Design: Lightweight, easy to carry for use
anywhere.
i. CDAC: Indian Language Corpora Initiative
6. User-Friendly Interface: Simple navigation with a
ii. Mozilla TTS: Open Source Text-to-Speech
2.4-inch TFT touchscreen.
Engine
7. Long Battery Life: Powered by a 3.7V 2000mAh
iii. Kaldi ASR Toolkit
Li-Ion battery.
iv. Google Translate Research Papers
8. Wi-Fi Connectivity: For syncing updates and new
v. IIT Madras NLP Research
languages.
vi. Android Java Development Guide
I. Applications
1. Healthcare: Assist doctors in understanding patients
speaking regional languages.
2. Tourism: Help travelers communicate with locals.
3. Government Services: Enable multilingual
support in public service centers.
4. Education: Bridge communication between
teachers and students from different states
K. Future Scope
1. Adding more languages and dialects.
2. AI-powered adaptive translation.
3. Cloud sync for logs and remote learning.
4. Integrating with wearable tech like smart glasses
or earbuds.
L. Conclusion
The Multilingual Translation Device showcases how
embedded systems and AI can combine to solve real-
world communication problems. By focusing on Indian
languages, offline operation, and ease of use, the
device presents a practical solution for everyday
multilingual interactions. Its success in testing proves
its viability for public service, and future enhancements
could position it as a mainstream communication tool
in diverse domains.
Page 4 of 6
Page 5 of 6
Page 6 of 6
