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German (Germany) Call Center Speech Dataset for Healthcare
The audio dataset comprises call center conversations for the Healthcare domain, featuring native German speakers from Germany. It includes speech data, detailed metadata and accurate transcriptions.
Category
Unscripted Call Center Conversations
Total Volume
30 Speech Hours
Last updated
Jun 2024
Number of participants
60
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About this Off-the-shelf Speech Dataset
Introduction
Welcome to the German Call Center Speech Dataset for the Healthcare domain designed to enhance the development of call center speech recognition models specifically for the Healthcare industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.
Speech Data
This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Healthcare domain, designed to build robust and accurate customer service speech technology.
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Speakers:
60 expert native German speakers from the FutureBeeAI Community.
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Regions:
Different states/provinces of Germany, ensuring a balanced representation of German accents, dialects, and demographics.
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Participant Profile:
Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.
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Conversation Nature:
Unscripted and spontaneous conversations between call center agents and customers.
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Call Duration:
Average duration of 5 to 15 minutes per call.
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Formats:
WAV format with stereo channels, a bit depth of 16 bits, and a sample rate of 8 and 16 kHz.
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Environment:
Without background noise and without echo.
Topic Diversity
This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.
•Appointment Scheduling
•New Patient Registration
•Surgery Consultation
•Consultation regarding Diet, and many more
•Appointment Reminder
•Health and Wellness Subscription Programs
•Lab Tests Results
•Health Risk Assessments
•Preventive Care Reminders, and many more
This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.
Transcription
To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:
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Speaker-wise Segmentation:
Time-coded segments for both agents and customers.
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Non-Speech Labels:
Tags and labels for non-speech elements.
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Word Error Rate:
Word error rate is less than 5% thanks to the dual layer of QA.
These ready-to-use transcriptions accelerate the development of the Healthcare domain call center conversational AI and ASR models for the German language.
Metadata
The dataset provides comprehensive metadata for each conversation and participant:
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Participant Metadata:
Unique identifier, age, gender, country, state, district, accent and dialect.
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Conversation Metadata:
Domain, topic, call type, outcome/sentiment, bit depth, and sample rate.
This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of German call center speech recognition models.
Usage and Applications
This dataset can be used for various applications in the fields of speech recognition, natural language processing, and conversational AI, specifically tailored to the Healthcare domain. Potential use cases include:
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Speech Recognition Models:
Training and fine-tuning speech recognition models for German.
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Speech Analytics Models:
Building speech analytics models to extract insights, identify patterns, and glean valuable information from customer conversation, enables data-driven decision-making and process optimization within the Healthcare sector.
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Smart Assistants and Chatbots:
Developing conversational agents and virtual assistants for customer service in the Healthcare industries.
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Sentiment Analysis:
Analyzing customer sentiment and improving customer experience based on call center interactions.
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Generative AI:
Training generative AI models capable of generating human-like responses, summaries, or content tailored to the Healthcare domain.
Secure and Ethical Collection
•Our proprietary data collection and transcription platform, “Yugo” was used throughout the process of this dataset creation.
•Throughout the data collection process, the data remained within our secure platform and did not leave our environment, ensuring data security and confidentiality.
•The data collection process adhered to strict ethical guidelines, ensuring the privacy and consent of all participants.
•It does not include any personally identifiable information about any participant, which makes the dataset safe to use.
•The dataset does not contain any copyrighted content.
Updates and Customization
Understanding the importance of diverse environments for robust ASR models, our call center voice dataset is regularly updated with new audio data captured in various real-world conditions.
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Environmental Conditions:
Custom collection in specific environmental conditions upon request.
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Sample Rates:
Customizable from 8kHz to 48kHz.
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Transcription Customization:
Tailored to specific guidelines and requirements.
License
This Healthcare domain call center audio dataset is created by FutureBeeAI and is available for commercial use.
Use Cases
Call Center Conversational AI
ASR
Chatbot
Language Modelling
TTS
Speech Analytics
Dataset Sample(s)
Samples will be available soon!
Contact us to get the samples immediately for this dataset.
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Dataset Demographics
Language
German
Language code
de
Country
Germany
Accents
Berlin,...more
Gender Distribution
M:60, F:40
Age Group
18-70
Audio File Details
Environment
Silent, Noisy
Bit Depth
16 bit
Format
wav
Sample rate
8khz & 16khz
Channel
Stereo
Audio file duration
5-15 minutes
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