GAN-based framework for improving Automatic Speech Recognition in low-resource languages.
# Improving Low-Resource Speech Recognition with GANs
## Overview
This project focuses on improving Automatic Speech Recognition (ASR) for low-resource languages using Generative Adversarial Networks (GANs).
The goal is to address the challenge of limited speech data by generating high-quality synthetic speech samples and applying advanced augmentation techniques to improve recognition performance.
---
## Problem Statement
Many languages lack sufficient speech datasets for training robust Automatic Speech Recognition systems.
This project aims to:
- Generate synthetic speech data
- Improve speech recognition accuracy
- Reduce Word Error Rate (WER)
- Enhance multilingual speech processing
- Support low-resource languages
---
## Key Features
- GAN-Based Speech Generation
- Multilingual Text-to-Speech (TTS)
- CycleGAN-Based Voice Conversion
- SpecAugment Data Augmentation
- Cross-Lingual Transfer Learning
- Automatic Speech Recognition Enhancement
---
## Technologies Used
- Python
- GANs
- Speech Processing
- Automatic Speech Recognition (ASR)
- CycleGAN
- SpecAugment
- Multilingual TTS
---
## Methodology
### Data Augmentation
- Synthetic Speech Generation
- Voice Conversion
- SpecAugment Transformations
### Model Enhancement
- GAN-Based Data Generation
- Cross-Lingual Knowledge Transfer
- Multilingual Speech Processing
### ASR Optimization
- Improved Training Dataset Diversity
- Enhanced Generalization
- Reduced Recognition Errors
---
## Results
### Key Achievements
- Up to 19% reduction in Word Error Rate (WER)
- Improved multilingual speech recognition
- Better performance on low-resource languages
- Enhanced cross-lingual transfer capability
---
## Applications
- Voice Assistants
- Speech-to-Text Systems
- Multilingual AI Applications
- Accessibility Tools
- Language Preservation Systems
---
## Future Improvements
- Transformer-Based ASR Models
- Real-Time Speech Recognition
- Additional Language Support
- Edge Device Deployment
--- …