Autonomous Driving Point Cloud
Deepannotate• Telugu -CONV-ASR • —
High-quality Telugu conversational speech dataset designed for training accurate and scalable ASR systems. Captures real-world, unscripted conversations across diverse speakers and environments.
Data Collection
Natural, unscripted conversations capturing real-world speaking conditions and diversity.
Annotation
Accurate verbatim transcription with speaker labeling, timestamps, and linguistic consistency.
Quality Assurance
Multi-level validation combining automated checks and expert human review.
Data Delivery
Structured, scalable datasets delivered securely and ready for model training.
SAMPLE PREVIEW
Football Match - Commentary
Speaker 1 • VIDEO • MP4
SMPL-001-speaker1.wav
705 • 22100 • MONO
SAMPLE ENTITIES
| Dataset ID | Telugu-SingleVoice-ASR |
| License | CC BY-NC 4.0 |
| Annotation Type | Transcription | Timestamp-Aligned Transcription |
| Languages | Telugu |
| Collection Method | Single-speaker recordings across diverse real-world environments |
| Hardware | Lapel microphones and portable audio recorders |
Topics Covered
Designed to support real-world speech AI and ASR model development
Core Applications
- Automatic Speech Recognition (ASR Training)
- Conversational Speech Understanding
- Voice-Based AI Systems
Language Intelligence
- Low-Resource Language Modeling (Telugu)
- Code-Mixed & Code-Switched Speech
- Multilingual Adaptation
Audio Processing
- Speaker Segmentation & Identification
- Acoustic & Phonetic Modeling
- Noise & Speech Pattern Analysis
Quality Assurance Process
Multi-level validation ensuring accuracy and consistency
Compliance & Data Review
Secure, ethical, and regulation-aligned data practices
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