ID int64 1 7 | Audio stringclasses 7
values | Text stringclasses 7
values |
|---|---|---|
1 | audio/1.wav | text/1.txt |
2 | audio/2.wav | text/2.txt |
3 | audio/3.wav | text/3.txt |
4 | audio/4.wav | text/4.txt |
5 | audio/5.wav | text/5.txt |
6 | audio/6.wav | text/6.txt |
7 | audio/7.wav | text/7.txt |
Italian Speech Dataset for recognition task
Dataset comprises 499 hours of telephone dialogues in Italian, collected from 670+ native speakers across various topics and domains, achieving an impressive 98% Word Accuracy Rate. It is designed for research in automatic speech recognition (ASR) systems.
By utilizing this dataset, researchers and developers can advance their understanding and capabilities in natural language processing (NLP), speech recognition, and machine learning technologies. - Get the data
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Metadata for the dataset
- Audio files: High-quality recordings in WAV format
- Text transcriptions: Accurate and detailed transcripts for each audio segment
- Speaker information: Metadata on native speakers, including gender and etc
- Topics: Diverse domains such as general conversations, business and etc
Frequently Asked Questions
What types of conversations are included in this Italian speech dataset?
The dataset contains real-world telephone conversations between native Italian speakers, covering natural dialogue patterns rather than isolated speech commands. The recordings represent everyday conversational scenarios, making the data useful for training AI systems that need to understand spontaneous Italian speech and realistic communication patterns.
Who can benefit from this Italian speech recognition dataset?
The dataset is useful for speech technology companies, AI researchers, universities, conversational AI developers, call automation providers, and organizations building Italian-language voice applications. It can support the development of transcription systems, voice assistants, customer service automation, and language understanding models.
How was the Italian speech data collected?
The recordings were collected through crowdsourcing platforms, allowing the dataset to include authentic conversations from different speakers."
This dataset is a valuable resource for researchers and developers working on speech recognition, language models, and speech technology.
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