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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

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  • Hours
    1,100+
  • Speakers
    1,400+
  • Sentence Accuracy Rate
    95%
  • 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
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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)
Source and collection methodology: Data was collected by a partner of Unidata.

Dataset Use Cases

  • Call Centers & Customer Service

    Enhancing American Telephone Dialogue Recognition

    American Speech Recognition Dataset contains authentic audio recordings from real conversations. Speech samples reflecting regional accents and conversational speech help call centers improve recognition systems and voice technology. This ensures more accurate audio transcriptions, faster call handling, and better customer support experiences in U.S.-based services.

  • AI & Machine Learning Research

    Training Models with High-Quality Audio Data

    This speech recognition dataset provides reliable training data for machine learning and deep learning models. It consists of high-quality audio samples from different speakers, paired with transcriptions. Researchers use it to develop recognition technology capable of handling varied speech patterns, enabling voice recognition with high accuracy across applications.

  • Multilingual & Cross-Language Projects

    Supporting Natural Language Processing in English

    The American audio dataset contributes to multilingual speech initiatives by providing clean voice data in the English language. Developers combine it with different languages for natural language tasks, speech translation, and automatic speech recognition. Its diverse speech samples ensure adaptability, making it essential for global voice recognition systems.

  • Commercial & Industrial Applications

    Deploying Speech Recognition in Real Scenarios

    Businesses use such datasets in commercial use cases such as smart assistants, transcription platforms, and voice recognition tools. Since the database contains human voices with varied speech patterns and voice recordings, it improves audio quality and enhances the performance of recognition systems in real-world spoken language tasks.

What is included in the American Speech Recognition Dataset?
This dataset consists of 1,136 hours of telephone dialogues recorded by 1,416 speakers across the United States. The audio files are provided in WAV format with annotations including transcriptions, speaker ID, gender, and age.
What is this dataset used for?
The dataset is designed for training automatic speech recognition systems, NLP models, and voice assistants. It can also be applied in customer service automation, emotion recognition, and voice command technologies.
How was the data collected?
The data was created through structured data collection using mobile devices under indoor conditions with low background noise. This ensures high-quality audio recordings suitable for speech recognition technology and voice analysis.
Is it possible to request a custom dataset?
Yes, Unidata supports requests for custom datasets. You can specify requirements such as speaker demographics, recording conditions, or annotation formats, making it easier to train more precise recognition systems and voice technology models.
Still have questions about using Unidata datasets? Read our user-guides

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