Korean Call-Center Database

Project Overview:

Objective

The “Korean Call-Center Database” initiative was aimed at developing a comprehensive dataset for enhancing speech recognition models tailored for call center environments. The primary goal was to enable these models to effectively comprehend and process varied accents, dialects, and speech patterns encountered in Korean call centers, thereby improving customer interaction and response efficiency.

Scope

This ambitious project encompassed collecting a vast array of Korean speech recordings from diverse sources, including call center interactions, professional voice actors, and public speech datasets. The project also involved extensive annotation to accurately identify nuances in speech, tone, and context.

Korean Call-Center Database
Korean Call-Center Database
Korean Call-Center Database
Korean Call-Center Database

Sources

  • Call Center Interactions: Utilized recordings from various Korean call centers, ensuring a realistic and practical dataset.
  • Professional Voice Actors: Engaged with voice actors to produce controlled, high-quality speech samples.
  • Public Speech Datasets: Integrated public domain datasets to enhance diversity in speech patterns.
Korean Call-Center Database
Korean Call-Center Database

Data Collection Metrics

  • Total Speech Recordings: 20,000 recordings.
  • Call Center Interactions: 10,000 recordings.
  • Voice Actors: 6,000 recordings.
  • Public Domain Datasets: 4,000 recordings.

Annotation Process

Stages

  1. Speech Contextualization: Annotating each recording with details about the conversation’s context, caller’s mood, and speech clarity.
  2. Metadata Annotation: Including metadata like call duration, speaker identification, and speech clarity ratings.

Annotation Metrics

  • Recordings with Contextual Annotations: 20,000.
  • Metadata Annotation: 15,000 recordings.
Korean Call-Center Database
Korean Call-Center Database
Korean Call-Center Database
Korean Call-Center Database

Quality Assurance

Stages

Annotation Verification: A rigorous review process by linguistic experts to ensure the accuracy of annotations.
Data Quality Control: Removal of inaudible or irrelevant recordings to maintain dataset quality.
Data Security: Adherence to strict data privacy laws and ethical guidelines in handling sensitive call center recordings.

QA Metrics

  • Verified Annotations: 5,000 recordings.
  • Data Cleansing: Ongoing process of quality control.

Conclusion

The “Korean Call-Center Database” project stands as a testament to our commitment to providing high-quality, diverse datasets for the evolving needs of AI in speech recognition. This dataset is not just a collection of voice recordings; it’s a bridge between technology and the intricate nature of human speech, especially in a customer service context. It is poised to revolutionize the way AI interfaces with human voices in call centers, enhancing both customer experience and operational efficiency.

quality dataset

Quality Data Creation

Guaranteed TAT​

Guaranteed TAT

ISO 9001:2015, ISO/IEC 27001:2013 Certified​

ISO 9001:2015, ISO/IEC 27001:2013 Certified

HIPAA Compliance​

HIPAA Compliance

GDPR Compliance​

GDPR Compliance

Compliance and Security​

Compliance and Security

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