Natural Devanagari Character Digit Audio Dataset - DEVA-AUDIO
Description
This dataset presents a collection of human-recorded audio samples of Devanagari characters designed for speech processing and CAPTCHA-based security applications. The dataset consists of isolated character-level recordings, including vowels, consonants, and digits, spoken by multiple contributors under varied recording conditions. Unlike synthetic speech datasets, all recordings are natural, capturing real-world variations in pronunciation, pitch, and background noise. The dataset aims to support research in automatic speech recognition (ASR), human-computer interaction, and accessible CAPTCHA systems, particularly for Indian language users. With over 10,000 audio samples, this dataset provides a valuable resource for developing robust and inclusive speech-based systems.A Python-based processing pipeline is implemented for audio format conversion (M4A to MP3), pause-based segmentation, and systematic organization of audio samples. A total of 10,675 validated audio samples are generated, including 7,108 character samples and 3,567 digit samples.
Files
Steps to reproduce
Description: • CHAR → Devanagari characters (folders 1–23) • DIGIT → Devanagari digits (0–9) • Each folder contains audio files (5 samples per class) • dataset.csv contains metadata (file path, label, type) Dataset Structure DATASET/ │ ├── CHAR/ │ ├── 1/ │ ├── 2/ │ ├── ... │ ├── 23/ │ ├── DIGIT/ │ ├── 0/ │ ├── 1/ │ ├── ... │ ├── 9/ │ └── dataset.csv Data Collection Methodology 1. Selection of Devanagari character set 2. Training of data collectors 3. Recording using: o Collar microphone o Mobile recording applications 4. Storage in mobile devices 5. Compilation and labeling of dataset ________________________________________ 📊 Dataset Statistics • Total Samples: 10,675 (original dataset) • Subset Used: Balanced dataset (5 samples per class) • Categories: o Characters o Digits • Type: Natural Human Speech
Categories
Funders
- Kavayitri Bahinabai Chaudhari North Maharashtra University under the Vice Chancellor Research Motivation Scheme (VCRMS) for the academic year 2025–26, Ref.No. KBCNMU/11A/VCRMS-SCI-2024-25/152/2025 Dated : 11/03/2025Grant ID: Ref.No. KBCNMU/11A/VCRMS-SCI-2024-25/152/2025 Dated : 11/03/2025