ID int64 1 5 | Audio stringclasses 5
values | Text stringclasses 5
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 |
American Speech Dataset for recognition task
Dataset comprises 1,136 hours of telephone dialogues in American, collected from 1,416 native speakers across various topics and domains, achieving an impressive 95% Sentence 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
How was this American telephone dialogues dataset collected?
Recordings were gathered via crowdsourcing platforms using Android smartphones and iPhones, capturing natural telephone-style conversations from native U.S. speakers.
Why does speaker diversity matter for American speech datasets like this one?
Wider variation in accents, ages, and speaking styles reduces recognition errors and improves model generalization across real-world callers, which is why this set spans 1,416 speakers rather than a narrow sample.
What types of conversations are included in this American speech recognition dataset?
This American speech recognition dataset contains real-world telephone dialogues between native speakers across the United States. The conversations reflect natural speaking patterns and are designed to support speech recognition, natural language processing (NLP), and conversational AI research.
This dataset is a valuable resource for researchers and developers working on speech recognition, language models, and speech technology.
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