American Speech Recognition Dataset
The dataset includes 1,136 hours of annotated telephone dialogues from 1,416 native speakers across the United States, providing high-quality audio recordings, transcriptions, and speaker metadata to support speech recognition systems, NLP tasks, and machine learning models requiring diverse American speech datasets
-
- Hours
- 1,100+
-
- Speakers
- 1,400+
-
- Sentence Accuracy Rate
- 95%
- NLP
- LLM
- Machine Learning
- Audio Processing
- ASR
- Voice Recognition
The dataset includes 1,136 hours of annotated telephone dialogues from 1,416 native speakers across the United States, providing high-quality audio recordings, transcriptions, and speaker metadata to support speech recognition systems, NLP tasks, and machine learning models requiring diverse American speech datasets
- NLP
- LLM
- Machine Learning
- Audio Processing
- ASR
- Voice Recognition
-
- Hours
- 1,100+
-
- Speakers
- 1,400+
-
- Sentence Accuracy Rate
- 95%
Dataset Info
| Characteristic | Data |
| Description | Audio of telephone dialogues in American for training NLP models in real-world conversational scenarios. |
| Data types | Audio |
| Tasks | Speech recognition, NLP |
| Country | the United States(USA) |
| Hours of telephone dialogue | 1,136 |
| Number of speakers | 1,416 |
| Labeling | Annotation (text content, speaker's ID, gender, age and other attributes) |
| Gender | Male (45%), Female (55%) |
| Recording device | Android smartphone, iPhone |
Statistics
-
- Distribution by gender
Technical
Characteristics
| Characteristic | Data |
| Audio Format | Wav |
| Sampling Rate | 16kHz |
| Number of Channels | Mono |
| Bit Depth | 16 bit |
| Recording condition | Low background noise (indoor) |
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