Hispanic English Call-Center Dataset

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Project Overview:

Objective

The “Hispanic English Call-Center Dataset” initiative is designed to build a comprehensive dataset for improving natural language processing (NLP) and voice recognition systems in call centers. This dataset focuses on enhancing customer service interactions by understanding and accurately processing Hispanic English accents.

Scope

This project entails recording and annotating a wide range of call-center conversations. These include interactions from various customer service scenarios, capturing the diversity and nuances of Hispanic English accents.

Sources

Audio recordings were sourced from participating call centers with a significant Hispanic customer base. Additionally, these recordings were collected to better understand the communication patterns and preferences of Hispanic customers. Furthermore, the goal was to analyze the effectiveness of customer service interactions and identify areas for improvement. Moreover, the recordings were transcribed and analyzed using linguistic and sentiment analysis techniques.
Data collection was conducted with strict adherence to privacy laws and ethical standards. Moreover, we ensured confidentiality and consent throughout the process.
Data collection was conducted with strict adherence to privacy laws and ethical standards. Furthermore, we ensured confidentiality and consent throughout the process. As a result, the collected data successfully generated a rich and diverse set of audio interactions, which reflect the nuances of English communication influenced by Hispanic cultural and linguistic backgrounds.

Data Collection Metrics

Total Conversations Recorded: 25,000
Sources of Data:
In-house Call-Center Recordings: 15,000
Collaboration with Partnering Call Centers: 7,000
Synthetic Conversations Generated Through Voice Actors: 3,000

Annotation Process

Stages

Speech Recognition Annotation: Tagging each conversation with detailed transcriptions, including nuances of Hispanic English accents.
Intent and Response Classification: Additionally, we will be classifying each part of the conversation based on customer intent and call-center response. This comprehensive classification ensures that all interactions are systematically organized and analyzed.
Emotional Tone Analysis: Furthermore, we will be annotating the emotional tone of both the customer and the agent throughout the conversation. By doing so, we can gain deeper insights into the emotional dynamics of each interaction.

Annotation Metrics

Conversations with Detailed Transcriptions: 25,000
Intent and Response Tags: 25,000
Emotional Tone Annotations: 25,000

Quality Assurance

Stages

Annotation Review: By implementing a rigorous review process conducted by linguistic experts, we can ensure the accuracy of annotations. Furthermore, this thorough review helps in maintaining high-quality standards.
Data Quality Control: In addition to the review process, identifying and rectifying any discrepancies in transcriptions and annotations is crucial. Consequently, this step enhances the overall reliability of the data.
Data Privacy Compliance: Moreover, ensuring that all data collection and annotation processes comply with data privacy laws and ethical standards is essential. By adhering to these regulations, we can protect individuals’ privacy and maintain trust.

QA Metrics

Reviewed and Validated Annotations: 2,500 (10% of total)
Data Refinement: Ongoing process of enhancing data quality

Conclusion

The “Hispanic English Call-Center Dataset” is a pivotal resource for advancing NLP and voice recognition technologies in customer service environments. This dataset, with its focus on Hispanic English accents, provides invaluable insights into customer-agent interactions. It enables the development of more accurate and efficient customer service solutions, fostering better understanding and communication in call centers serving Hispanic English-speaking customers.