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 and Speech-to-Speech, allowing users to choose the
designed to provide seamless real-time language most appropriate form of interaction based on their
translation for multiple Indian languages. Our project context and preferences. Developed using Java and
integrates speech recognition, natural language integrated with a mobile application, the system offers a
processing, and audio synthesis to enable speech-to- user-friendly interface and is optimized for low-resource
speech, speech-to-text, and text-to-text translations. settings. The primary focus of the device is on accuracy,
Built with a compact embedded system and powered by contextual relevance, and speed, ensuring that the
a Java-based mobile application, the device allows users translated output maintains the intended meaning and
to communicate effortlessly across language barriers. tone of the original communication. It also prioritizes
This system emphasizes offline translation capabilities, ease of use, making it accessible for people from
regional language support, and cost-effective different educational backgrounds, including those who
deployment. The project demonstrates potential in may not be tech-savvy.The proposed system has vast
public service sectors, tourism, and healthcare to applications in sectors such as healthcare, education,
promote inclusive communication. 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
Keywords communities and economies.
Language Translation, Speech Recognition, Indian
Languages, NLP, Java Application, Offline
Communication B. Literature Survey
Over the years, various translation technologies have
emerged, ranging from rule-based models to advanced
A. Introduction neural machine translation (NMT) systems such as
In today's globalized and digitally connected world, the Google Translate and Microsoft Translator. These
ability to communicate across language boundaries has platforms leverage deep learning and large datasets to
become increasingly vital. This is particularly relevant deliver accurate translations but often rely on
in multilingual nations like India, where linguistic continuous internet access and provide limited support
diversity is both rich and widespread. With 22 for regional Indian languages. Government-led
officially recognized languages and hundreds of
initiatives like Bhashini and TDIL have contributed
dialects, India presents a unique challenge: enabling
seamless communication among individuals who do valuable linguistic resources, yet their integration into
not share a common language. Language barriers in real-time, offline-capable devices remains limited.
such settings can hinder access to essential services, Recent advancements in speech recognition, attention
limit educational and economic opportunities, and mechanisms, and transformer-based architectures have
create significant social divides. 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
To address these challenges, this paper introduces the single, user-friendly device tailored for India’s diverse
design and development of a Multilingual Translation
linguistic landscape.
Device, an intelligent system engineered to facilitate
real-time communication between speakers of different
Indian languages. Unlike many existing translation C. Background
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 Text-to-Text, Text-to-Speech,
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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, offline 3. Audio Interface:
devices that support multiple input and output formats. Microphone: High-sensitivity omnidirectional
This highlights the need for an accessible, portable microphone for speech input.
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 text from Testing & Optimization: Conducted real-life
3 Text Translation source to target conversations to tune accuracy, latency, and
language using usability.
translation engine
G. Implementation
The multilingual translation device is implemented using
4 Voice Output Repeat the control
an ESP8266 microcontroller, which handles processing
loop steps until the tasks and connects to other components, including a 2.4-
mission objectives inch TFT display, microphone, 3W speaker, and a 16GB
are achieved. MicroSD card for offline storage. The firmware is
developed in C/C++ to control speech recognition,
translation, and output. Pre-trained neural machine
5 Mobile Sync Display translations on translation (NMT) models are used for text translation,
connected mobile app while speech-to-text and text-to-speech models handle
for user convenience. audio input and output. The device also communicates
with a Java-based mobile application, allowing users to
update translation models and sync data via Wi-Fi.
Extensive testing is conducted to optimize translation
accuracy, reduce latency, and ensure seamless
performance in real-world use
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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.
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