ID int64 | Language string | Format string | Minutes string |
|---|---|---|---|
1 | Vietnamese | MP3 | 38:31:00 |
Vietnamese Speech Dataset
Dataset comprises 10+ hours of telephone dialogues in Vietnamese, collected from 20 native speakers across various topics and domains. It is designed for research in speech recognition, focusing on various recognition models, primarily aimed at meeting the requirements for automatic speech recognition (ASR) systems.
By utilizing this dataset, researchers and developers can advance their understanding and capabilities in transcribing audio, and natural language processing (NLP). - Get the data
The dataset contains diverse audio files that represent different accents and dialects, making it a comprehensive resource for training and evaluating recognition models.
Frequently Asked Questions
What type of conversations are included in this dataset?
The dataset contains real telephone dialogues in Vietnamese recorded in conversational scenarios. These recordings provide natural speech patterns and interaction flows, making them suitable for researchers developing systems that need to understand everyday spoken Vietnamese rather than only isolated words or predefined commands.
Who can benefit from this Vietnamese speech dataset?
The dataset is valuable for AI researchers, speech recognition developers, universities, conversational AI companies, and organizations building Vietnamese-language voice applications. It can support projects involving automatic speech recognition, transcription, voice assistants, customer communication analysis, and natural language processing.
How was the Vietnamese speech data collected?
The recordings were collected through crowdsourcing platforms, allowing the dataset to include authentic telephone conversations from Vietnamese speakers."
💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.
This dataset is essential for anyone looking to improve speech recognition technology and develop more effective automatic speech systems.
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