Building Automatic Speech Recognition (ASR) Datasets in 6 Indian Languages
Over 2,000 Hours of Speech Data Collected, Segmented & Transcribed to Train Accurate and Inclusive ASR Models
A leading technology company was developing automatic speech recognition (ASR) models to support multiple Indian languages. Their goal was to create accurate and inclusive voice-driven applications capable of understanding diverse dialects, accents, and speech patterns across India.
However, their in-house team struggled with collecting high-quality speech data, segmenting long recordings, and ensuring precise transcriptions for training ASR models. They needed a specialized data partner who could handle large-scale multilingual speech datasets with consistency, linguistic accuracy, and quality assurance.
That’s when they partnered with Dserve AI.
Project Objective
The primary goal of this project was to collect, segment, and transcribe large-scale audio datasets in six Indian languages to train high-performing ASR models. Specifically, the objectives included:
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Collect High-Volume Data: Gather over 2,000 hours of diverse speech data from real speakers across India.
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Ensure Linguistic Diversity: Capture various dialects, accents, and regional variations to create inclusive datasets.
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Accurate Segmentation and Transcription: Process raw audio into well-segmented files with precise transcriptions for AI model training.
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Enhance ASR Accuracy: Enable the client’s ASR system to recognize speech accurately across languages, accents, and contexts.
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Deliver Ready-to-Use Datasets: Provide structured, clean datasets that could be directly used for training, fine-tuning, and evaluation of speech recognition models.
Key Challenges
Creating high-quality, multilingual ASR datasets posed several unique challenges.
Challenges Overview:
| Challenge | Description |
|---|---|
| Language Diversity | Each of the six languages had multiple dialects and accents, requiring careful planning to cover representative speech samples. |
| Large Data Volume | Collecting and processing over 2,000 hours of audio while maintaining accuracy and consistency across languages was resource-intensive. |
| Annotation Accuracy | Transcriptions had to be highly precise, requiring expert linguists to verify every sentence and ensure consistency. |
| Speaker Diversity | Ensuring the dataset included speakers from different age groups, genders, and regions was critical to train unbiased ASR models. |
| Audio Quality Variability | Recordings from different devices and environments introduced noise and inconsistencies, making cleaning and normalization essential. |
Our Solution
To address these challenges, we implemented a structured, end-to-end pipeline for dataset creation:
Data Collection:
- Recruited speakers across multiple regions to ensure linguistic and demographic diversity.
- Captured natural and scripted speech using both mobile devices and studio recordings to cover varied audio conditions.
Ensured ethical data collection with consent forms and privacy compliance.
Segmentation & Transcription:
- Segmented long audio recordings into manageable, meaningful chunks for ASR training.
- Transcribed audio with high accuracy, incorporating regional spelling variations and phonetic nuances.
Used native linguists and automated verification tools for cross-checking transcription quality.
Quality Assurance:
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Multi-layer QA process including linguist review, automated checks, and consistency audits.
Eliminated noise, silences, and poor-quality recordings to maintain clean dataset standards.
Data Delivery:
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Provided the client with a structured, ready-to-use dataset, including metadata for each recording (language, speaker age/gender, region).
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Ensured scalability, allowing the dataset to be extended for future ASR projects.
Dataset Highlights
| Metric | Value |
|---|---|
| Total Audio Hours | 2,000+ |
| Languages Covered | 6 Indian Languages |
| Speaker Diversity | 500+ (across age, gender & region) |
| Annotation Accuracy | 99% |
| Segmented Audio Files | 50,000+ |
| Delivery Format | WAV, MP3, CSV, JSON |
| Noise & Silence Removal | 100% Cleaned |
The project delivered a comprehensive multilingual ASR dataset that enabled the client to develop high-performing speech recognition models. Key outcomes included:
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2,000+ Hours of Speech Data: A large-scale, high-quality dataset covering six Indian languages.
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Diverse Speaker Representation: Included variations in age, gender, accent, and dialect for inclusive AI models.
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Improved ASR Accuracy: Enabled more precise recognition of regional speech patterns, reducing errors in real-world applications.
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Faster Model Training: Ready-to-use, segmented, and annotated data minimized preprocessing time for AI engineers.
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Scalable Dataset Framework: Built a framework for future data expansion and continuous model improvement.
The dataset provided was of exceptional quality. It captured linguistic nuances effectively and helped us train our ASR models with confidence. The team’s attention to detail and thoroughness made the process seamless.
Dr. Michael Anderson - Lead NLP Scientist
Conclusion
By meticulously collecting, segmenting, and transcribing over 2,000 hours of audio, we delivered a robust multilingual ASR dataset that addressed the client’s requirements for accuracy, diversity, and scalability. This dataset has empowered the client to enhance their speech recognition models across six Indian languages, improving accessibility and usability of voice-driven technologies for millions of users.
Get a Sample of Our High-Quality ASR Dataset
At Dserve AI, we specialize in providing high-quality, domain-specific datasets to power AI and machine learning solutions. From multilingual speech data to healthcare and computer vision datasets, our expertise ensures accuracy, diversity, and compliance at scale.
Experience the quality of our 2,000+ hours of ASR data before making a commitment. Get a sample of our meticulously collected, segmented, and transcribed dataset and see how it can accelerate your AI initiatives.
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