Multispeaker Hindi ASR Dataset

Data Collection

The dataset consists of Hindi audio recordings collected from diverse sources, capturing real-world variability across different speakers, recording environments, and devices. This multi-source approach ensures the dataset reflects the natural range of Hindi speech as it occurs in everyday contexts, from controlled studio settings to informal conversational environments.

Preprocessing

Audio files have undergone standard preprocessing steps including noise reduction, silence trimming, segmentation, and amplitude normalization to ensure consistency across recordings. Transcriptions have been cleaned and standardized to maintain alignment accuracy between audio segments and their corresponding text.

Annotation Quality

Each audio sample is paired with a corresponding text transcription, manually or automatically generated depending on the source. Annotation quality has been reviewed for consistency; however, minor variations may exist across different data sources and transcription methods. Users are advised to validate annotations for high-precision applications.

Speaker Diversity

The dataset includes multiple speakers with natural variations in accent, tone, speaking speed, and regional pronunciation, reflecting the linguistic diversity of Hindi across India. This speaker diversity makes the dataset particularly well-suited for training robust and generalized ASR models that perform reliably across a wide range of speaker profiles.

Use Cases

This dataset is well-suited for a range of speech and language processing tasks, including:

• Automatic speech recognition (ASR) system development
• Speech-to-text transcription modeling
• Speaker identification and verification
• Hindi language modeling and acoustic research
• Low-resource and multilingual NLP pipeline development

Limitations

• Speaker representation may be imbalanced, with certain accents, dialects, or demographics having fewer samples than others.
• Some recordings may contain background noise, overlapping speech, or audio artifacts that could affect model performance.
• Minor transcription inconsistencies may be present across different source batches.
• Additional cleaning, validation, or augmentation may be required depending on the specific application and accuracy requirements.