Developing Multilingual Natural Language Tool To

Support Literacy and Learning in Indian Education

Mrs G.Dianakamal M. Amrutha Sai M. Asha Priya
Assistant Professor Dept of CSE(AI&DS) Dept. of CSE (AI & DS)
Dept. of CSE (AI & DS) Vishnu Institute of Technology Vishnu Institute of Technology
Vishnu Institute of Technology Bhimavaram, AP Bhimavaram, AP
Bhimavaram, AP 21pa1a5403@vishnu.edu.in 21pa1a5407@vishnu.edu.in
dianakamal.g@vishnu.edu.in

K. Likhit Sai M. Poply Raja

Dept. of CSE (AI & DS) Dept. of CSE (AI & DS)
Vishnu Institute of Technology Vishnu Institute of Technology
Bhimavaram, AP Bhimavaram, AP
21pa1a5442@vishnu.edu.in 21pa1a5458@vishnu.edu.in

Abstract - This project aims to develop a Multilingual to provide real-time text translation between 24 Indian
Natural Language Tool to support literacy and learning languages and English. Using pyttsx3 and gTTS, the
in Indian education. The system leverages Google
program incorporates text-to-speech (TTS) features to
Translate's API for real-time translation across 24
Indian languages and English, with additional text-to- enhance its translation skills. This allows users to listen
speech functionality implemented using pyttsx3 and to translated content in their selected language.
gTTS. Built on Streamlit Python for an intuitive user
interface, the tool bridges linguistic barriers by Built on Streamlit Python for an intuitive and
providing seamless translation and accessible audio interactive user experience, the tool empowers
outputs. This innovative approach enhances educational educators, learners, and institutions to overcome
inclusivity, particularly benefiting regions with diverse linguistic barriers, creating a more inclusive
linguistic needs. By combining translation and speech educational environment. The system's ability to
synthesis, the tool facilitates effective communication combine text translation with speech synthesis allows it
and learning, demonstrating its potential to transform to cater to a wide audience, including those with visual
multilingual education in India. impairments or auditory learning preferences.
Keywords: Multilingual Translation, Text-to-Speech,
Google Translate API, Indian Languages, Streamlit
II. LITERATURE REVIEW
Python, pyttsx3, gTTS, Literacy Support, Educational
Inclusivity, Natural Language Processing
The integration of multilingual translation systems and
speech-to-text models into educational tools has
I. INTRODUCTION opened up new possibilities for bridging language
barriers and enhancing accessibility. This section
provides a review of key developments and research
India's diverse linguistic landscape presents unique related to language translation, speech synthesis, and
challenges in education, with students across the text-to-speech (TTS) technologies, which form the
country speaking a multitude of regional languages. backbone of the proposed system for multilingual
This linguistic diversity often creates barriers to natural language processing (NLP) and learning tools.
effective learning and literacy development. To address
this issue, there is a pressing need for tools that can Multilingual translation has been a central focus in
facilitate multilingual communication and support natural language processing (NLP) for decades, aiming
to make information accessible across different
education in multiple languages.
languages. Google Translate, one of the most widely
used translation systems, supports a vast array of
Creating a multilingual natural language tool to languages, including several Indian languages.
improve literacy and learning in Indian schools is the
goal of this project. In order to promote accessibility Text-to-speech (TTS) technologies have been
and inclusivity, the service uses Google Translate's API instrumental in making content accessible to visually
impaired individuals and learners in diverse
educational settings. Systems like pyttsx3 and Google IV. PROPOSED SYSTEM
Text-to-Speech (gTTS) have been widely adopted in
assistive technologies, where they convert written text The proposed system introduces an integrated platform
into speech, allowing users to engage with educational for multilingual text translation and speech synthesis
content audibly. TTS engines are particularly effective tailored for Indian languages and English. By
in inclusive education by providing an alternative mode leveraging the Google Translate API, Streamlit, and
of access to learning materials. However, generating pyttsx3, the system offers real-time translation and
natural-sounding speech in multiple languages has accessible text-to-speech (TTS) features to support
been a significant challenge, especially for Indian education, accessibility, and cultural preservation.
languages with complex phonetics and scripts.
Google Translate model provides seamless translation
Speech-to-text (STT) technologies have become between English and multiple Indian regional
increasingly important in enhancing verbal languages. It ensures contextually accurate and
communication in multilingual settings, especially in grammatically correct translations for educational and
educational environments where students and professional content. Streamlit Python enables an
instructors may speak different languages. Deep intuitive and interactive user interface for text input,
learning-based models, such as Wav2Vec2 and language selection, and result visualization. Pyttsx3
Whisper, have made significant strides in improving and gTTS implement speech synthesis to convert text
STT accuracy across various languages, including into natural-sounding audio, ensuring support for
regional Indian languages. visually impaired users or auditory learners, enhancing
inclusivity.

III. EXISTING SYSTEM Proposed Workflow: Users can input text or upload a
file via the Streamlit-based interactive interface. The
text may be in any supported language, including
The existing systems for language translation and text- Indian regional languages and English. The system
to-speech functionalities primarily rely on standalone uses Google Translate to detect the source language
translation tools and basic speech synthesis and translates the text into the selected target language
technologies. While these tools have been effective for in real time. The translated text is converted into audio
general-purpose applications, they face several using TTS engines (pyttsx3 or gTTS), enabling users to
limitations when applied to multilingual education and listen to the output in their chosen language. The
accessibility scenarios. translated text is displayed on the interface alongside
options for speech playback or stopping the audio. The
Google Translate, one of the most widely used system also ensures that users can download both the
translation systems, supports a vast array of languages, translated text and the generated speech for offline use.
including several Indian languages. Early approaches
to translation systems were rule-based, but statistical APPLICATIONS :
machine translation (SMT) and more recently, neural
machine translation (NMT) have significantly Education: Facilitates seamless translation and text-to-
improved translation quality by considering contextual speech for multilingual classrooms, helping students
information and sentence structures. grasp concepts in their native language.

Text-to-speech (TTS) technologies have been Training: Enables translation of training materials and
instrumental in making content accessible to visually customized assessments for corporate or professional
impaired individuals and learners in diverse development programs, supporting diverse workforce
educational settings. Systems like pyttsx3 and Google needs.
Text-to-Speech (gTTS) have been widely adopted in
assistive technologies, where they convert written text Research: Assists researchers in creating multilingual
into speech, allowing users to engage with educational survey questions, test materials, or data collection
content audibly. tools, improving outreach and inclusivity in diverse
communities.
Speech-to-text (STT) technologies have become
increasingly important in enhancing verbal Accessibility Tools: Supports auditory learning by
communication in multilingual settings, especially in providing real-time speech playback of translated or
educational environments where students and generated content.
instructors may speak different languages.
Cultural preservation: Promotes regional language
learning and usage by making translation and text-to-
speech tools available for less-dominant Indian Text to Speech: The translated text is fed into a speech
languages, ensuring their longevity in the digital era. synthesis engine (e.g., pyttsx3 or other compatible
tools) to produce natural-sounding audio in the selected
language. Users can stop speech and replay the speech
V. SYSTEM ARCHITECTURE as per their needs.

Accessibility Features: Users can listen to both the

original and translated text directly from the interface,
enhancing accessibility for visually impaired
individuals or auditory learners.

User Interaction and Interface: A minimalistic and

interactive Streamlit platform enables users to enter or
upload text, select the target language for translation,
and listen to the translated speech output.

System Workflow: Text is entered into the system. The

multilingual NLP model processes the input and
generates translated text. The text-to-speech engine
converts the translated text into speech. The translated
text and audio output are presented on the Streamlit
interface. Users can listen to the audio playback for
enhanced understanding.

Evaluation and Testing: Performance metrics such as

translation accuracy, semantic consistency, and
grammar are assessed. Accessibility testing ensures
that the tool is functional for visually impaired users
and accessible across diverse platforms.

Fig.1. System Architecture INTEGRATION OF MODULES

The proposed multilingual natural language processing

VI. METHODOLOGY
(NLP) tool for text translation and speech is designed
The methodology describes the structured approach with a modular architecture, where each module
used to develop the Multilingual Natural Language functions independently but integrates smoothly to
Processing (NLP) tool, integrating translation, text-to- deliver a cohesive, efficient, and scalable system. The
speech capabilities, and an accessible user interface to integration of modules ensures an intuitive user
support literacy and learning across Indian languages. experience and robust system performance.

Data Input: Users can input text via the Streamlit web MODULES OVERVIEW:
interface for translation and speech synthesis.
Preprocessing ensures input text is cleaned, tokenized, Input Module: Captures user input, such as text or
and formatted for seamless processing by the uploaded documents, and prepares it for translation and
translation and text-to-speech models. speech processing.

Translation Process: Text input is processed through a Translation Module: Uses the multilingual NLP model
multilingual NLP model (e.g., Wav2Vec2 or Gemini to translate input text into the target language.
Gen AI API), designed for translating Indian languages
to/from English. The tool generates translated text in Speech Generation Module: Converts the translated
the desired Indian language, ensuring semantic text into natural-sounding speech using a text-to-
accuracy and contextual relevance. speech engine.
User Interface Module: A Streamlit-based interface The overall workflow of the proposed multilingual
provides an interactive platform for inputting data, natural language processing system is as follows: The
selecting languages, and receiving output. user inputs a topic or uploads a text file. Alternatively,
the user can input speech, and the system transcribes it
into text using the speech recognition library. For text
input, the system directly processes the provided text.
For speech input, the transcribed speech is converted to
INTEGRATION WORKFLOW:
text using the speech recognition model. The text is
then passed through the Google Translate API, where it
User Input to Processing: Preprocessing ensures that is translated into the desired language. The translation
the input is compatible with the NLP model for is stored in a unified format for consistency. The
accurate translation. translated text is converted into speech using pyttsx3,
allowing users to listen to the content in the selected
Processing to Output: Output text is sent to the speech language. The translated text is displayed on the
generation module, where it is converted into speech in Streamlit interface for user review. Users can listen to
the selected language. the translated text in real-time or download the results
for future use.
Storage Integration: Translated text and generated
speech can be stored temporarily during the session.
Users have the option to save or export the output to a VII. RESULT AND DISCUSSION
local or cloud storage solution for future reference.

Module Interactions: Input Module interacts with the Standard performance metrics such as accuracy,
Translation Module, where input text is processed and precision, recall, F1-score, and Word Error Rate (WER)
translated into the desired language. The Translation were used to evaluate the "Multilingual Natural Language
Module interacts with the Speech Module, where Processing Tool for Text Translation and Speech". The
translated text is passed to the speech generation evaluation results are presented for both the text
module for audio conversion. The Speech Module translation and speech-to-text modules, along with a
interacts with the Accessibility Module, where the comparison of the proposed system's performance with
speech output is further processed to ensure existing translation and transcription solutions. The results
compatibility with various accessibility needs. The are visualized to aid interpretation.
Accessibility Module interacts with the Output
Module, where the audio output can be stored for RESULT OF TEXT TRANSLATION:
playback or later use based on user preferences.
The text translation module was evaluated on a
Technologies Used in Integration: Streamlit facilitates multilingual dataset containing English, Marathi, and
interaction between the user interface and backend Hindi text. The evaluation metrics include Translation
Accuracy and BLEU Score (Bilingual Evaluation
processes, ensuring smooth real-time processing.
Understudy). Table 1 summarizes the performance:.
Google Translation powers the core language
translation capabilities, enabling accurate translations
Table 2: Performance of Text Translation Module
across multiple languages. Pyttsx3 handles text-to-
speech conversion, providing real-time, high-quality
audio output.
Language Pair Translation Response
Benefits of Modular Integration: Each module operates Accuracy Time (in
independently, allowing for easy updates and (%) sec)
enhancements. The system can accommodate
additional features, such as multilingual support or
advanced analytics, without disrupting existing
Hindi to Marathi 98.8 5
functionalities. Issues in one module do not affect the
entire system, making debugging and maintenance
easier. Integration ensures that all modules work
together seamlessly to deliver a smooth and intuitive Telgu to kannada 92.5 5
user experience.

WORKFLOW:
Bengali to Gujrati 95.60 7
 Sr. System Translati Time F1-
No on (in Score
Accuracy sec) (%)
(%)
The results indicate that the system performs
exceptionally well with high translation accuracy (above
98%) for all language pairs, and it responds quickly,
providing translations in under one second on average.
1 Existing 95.5 5 -
RESULTS OF SPEECH-TO-TEXT CONVERSION: Translation
System
The speech-to-text module was evaluated on short
recorded audio files in English, Marathi, and Hindi. The
system was assessed on Word Error Rate (WER),
Precision, Recall, and F1-Score, utilizing Google’s 2 Existing Speech 97 100 -
automatic transcription system via speech-to-text to text System
conversion.

Table 3 presents the performance results:

3 Proposed System 98.7 7.2 94.1
Table 3: Performance of Voice Note Transcription
Module

The comparative analysis shows that the proposed system

outperforms existing systems, particularly in terms of
Langu WER Precision Recall F1-Score translation accuracy and speech-to-text performance, with
age (%) (%) (%) (%) significantly lower WER and faster response times.

Discussion
Marath 7.2 94.5 93.8 94.1
i The evaluation results demonstrate the effectiveness and
robustness of the proposed system. The handwritten text
recognition module, powered by CNN-based architecture,
shows exceptional accuracy even when faced with
Hindi 8.1 92.7 91.5 92.1 multilingual datasets and noisy handwriting samples.
Despite variations in handwriting styles and conditions,
the system maintains high Character Recognition
Accuracy (CRA) and Word Recognition Accuracy
Telgu 9.5 90.3 89.8 90.0 (WRA), showcasing its versatility and reliability in
diverse real-world scenarios.

Similarly, the voice transcription module performs well

The results demonstrate that the system performs well in across multiple languages, achieving a low Word Error
transcribing speech to text with relatively low WER, Rate (WER) and consistently high F1-Score values. The
particularly in English and Marathi. While the WER is NLP-based transcription system used for voice
slightly higher for Hindi, the transcription accuracy recognition has proven to be adaptable, delivering
remains impressive across all languages. accurate results even for languages with complex phonetic
structures. This outcome highlights the potential of
Comparative Analysis with Existing Systems speech-to-text systems in multilingual environments,
reinforcing the system’s utility for users in different
To validate the system’s performance, a comparative regions.
analysis was conducted against existing solutions. The
metrics compared include CRA, WRA, and WER. The comparative analysis between the proposed system
and existing solutions confirms its superiority, particularly
Table 4: Comparative Performance of the Proposed in terms of recognition accuracy, error rates, and response
System vs. Existing Systems time. The integration of CNN and NLP models has
significantly reduced WER and improved recognition
accuracy, proving the efficiency of the hybrid
architecture. The real-time processing capability,
multilingual support, and unifying platform design make
this solution highly adaptable for various domains such as Adding more Indian languages: Develop specialized
education, business, and note-taking applications. It offers models for language preservation, ensuring that
a promising tool for real-time transcription and dialects and lesser-known languages also receive
multilingual translation with applications ranging from attention, making the system a crucial tool for
classroom settings to professional environments. digitizing Indian languages.

Increasing Accuracy and Speed for Speech-to-Text:

Improve the accuracy of speech-to-text transcription,
VIII. CONCLUSION especially in noisy environments, by integrating more
advanced acoustic models and noise-reduction
algorithms. Explore the use of multi-modal models that
The developed system offers a novel and efficient can integrate contextual information and visual cues,
solution for real-time translation and speech-to-text improving transcription accuracy in settings where
conversion, with an emphasis on multilingual support and multiple speakers or background noise are present.
accessibility. By leveraging Google Translate for text
translation and pyttsx3 for text-to-speech functionality,
the system provides an accurate, fast, and user-friendly
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