Audio Datasets for Healthcare & Conversational AI

Home » Case Study » Audio Datasets for Healthcare & Conversational AI

Project Overview:

Objective

To compile a multifaceted audio dataset specifically designed for applications in Healthcare and Conversational AI, thereby facilitating advanced recognition, comprehension, and interaction capabilities.

Scope

To compile a multifaceted audio dataset specifically tailored for applications in Healthcare and Conversational AI, thus enabling enhanced recognition, comprehension, and interaction capabilities.

Sources

Electronic Health Records (EHRs) represent a crucial component, enabling the comprehensive accumulation of patient histories. Furthermore, EHRs facilitate seamless access to patient data, ensuring efficient management and continuity of care.
Patient Intake Forms: Additionally, these forms captured essential patient-reported information, ensuring that healthcare providers have accurate initial data.
Laboratory Test Results: Furthermore, these results productively provided key data points from various medical tests, contributing to informed medical decisions.
Clinical Trial Data: In addition, this data offered insights from controlled medical studies, thereby enhancing the understanding of treatment efficacy.

Data Collection Metrics

Total Audio Samples: 200,000
Patient-Doctor Consultations: 70,000
Health Helpline Recordings: 30,000
Medical Seminars and Lectures: 40,000
AI-User Interactions: 40,000
General Conversational Samples: 20,000

Annotation Process

Stages

Noise Reduction: Filtering out background noise and enhancing voice clarity are crucial for achieving high-quality audio recordings.
Segmentation: Breaking down long recordings into meaningful chunks can significantly enhance the listening experience and improve comprehension.
Transcription: Converting audio data into textual representation can be a complex process.
Entity Recognition: Highlighting medical terms, drug names, conditions, etc.
Intent Recognition: Categorizing the purpose behind each conversational interaction.

Annotation Metrics

Total Annotations: 600,000 (3 annotations per audio sample on average)
Noise-reduced Samples: 180,000
Segmented Chunks: 550,000
Transcribed Samples: 200,000
Entity Tags: 150,000
Intent Tags: 100,000

Quality Assurance

Expert Review:Medical professionals and linguistic experts meticulously reviewed the annotations for accuracy. Furthermore, they conducted thorough assessments to ensure the precision of the annotations.

Moreover, Consistency Checks: Automated checks to identify anomalies or inconsistencies in transcriptions are essential for ensuring data accuracy and reliability.

Additionally, Inter-annotator Agreement: Multiple annotators assessed a portion of the data to ensure consistent and reliable annotations.

QA Metrics

Annotations Reviewed by Experts: 60,000 (10% of total annotations)
Inconsistencies Identified and Corrected: 12,000 (2% of total annotations)

Conclusion

This ambitious undertaking, consequently, has yielded a robust audio dataset meticulously curated specifically for healthcare and conversational AI applications. Through meticulous collection and annotation, therefore, the dataset is poised to significantly bolster advancements in AI-driven healthcare solutions and conversational platforms alike.