Hinglish call-center Dataset

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Project Overview:

Objective

The “Hinglish Call-Center Dataset” initiative is designed to enhance customer service experiences and improve automated response systems. This project focuses on creating a rich dataset, combining Hindi and English (Hinglish), primarily for training advanced AI models in customer service applications.

Scope

This venture encompasses the collection and annotation of bilingual call center interactions. The dataset includes a diverse range of Hinglish conversations, covering various customer service scenarios.

Sources

Call Center Recordings: Authentic conversations between customers and agents.
Scripted Dialogues: Custom scripts acted out by bilingual speakers to cover a wide range of scenarios.
User-Generated Content: Voluntary submissions of Hinglish interactions from the public.

Data Collection Metrics

Total Conversations Recorded: 25,000
Call Center Recordings: 15,000
Scripted Dialogues: 7,000
User-Generated Content: 3,000

Annotation Process

Stages

Language Segmentation: Annotating the switch between Hindi and English within each conversation.
Context Tagging: Labeling the context and intent of the conversation.
Emotion Recognition: Identifying and labeling emotional tones.

Annotation Metrics

Conversations with Language Labels: 25,000
Context Tagging Completed: 25,000
Emotionally Annotated Conversations: 25,000

Quality Assurance

Stages

Annotation Review: A team of bilingual experts reviews the annotations for accuracy.
Data Cleansing: Removing irrelevant or low-quality recordings to ensure dataset integrity.
Data Security and Privacy: Ensuring compliance with data protection laws and ethical guidelines.

QA Metrics

Reviewed Annotations: 2,500 (10% of total)
Data Cleansing Initiatives: Continuous assessment and removal of subpar data.

Conclusion

The “Hinglish Call-Center Dataset” is a groundbreaking resource pivotal for advancing AI in customer service. With meticulously annotated bilingual conversations, it offers a versatile tool for developing sophisticated AI models that can seamlessly navigate Hinglish interactions, thereby revolutionizing customer service automation and enhancing user experience in bilingual environments.