.webp&w=3840&q=100)
.webp&w=640&q=100)
.webp&w=640&q=100)
.webp&w=640&q=100)
.webp&w=640&q=100)
.webp&w=640&q=100)
.webp&w=640&q=100)
We Use Cookies!!!
We use cookies to ensure that we give you the best experience on our website. Read cookies policies.
Turkish (Turkey) Call Center Speech Dataset for Retail & E-commerce
The audio dataset comprises call center conversations for the Retail & E-commerce domain, featuring native Turkish speakers from Turkey. 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
Get this Speech Dataset
About this Off-the-shelf Speech Dataset
Introduction
Welcome to the Turkish Call Center Speech Dataset for the Retail domain designed to enhance the development of call center speech recognition models specifically for the Retail 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 Retail domain, designed to build robust and accurate customer service speech technology.
•
Speakers:
60 expert native Turkish speakers from the FutureBeeAI Community.
•
Regions:
Different states/provinces of Turkey, ensuring a balanced representation of Turkish accents, dialects, and demographics.
•
Participant Profile:
Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.
•
Conversation Nature:
Unscripted and spontaneous conversations between call center agents and customers.
•
Call Duration:
Average duration of 5 to 15 minutes per call.
•
Formats:
WAV format with stereo channels, a bit depth of 16 bits, and a sample rate of 8 and 16 kHz.
•
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.
•Product Inquiry
•Return/Exchange Request
•Order Cancellation
•Refund Request
•Membership/Subscriptions Enquiry
•Order Cancellations, and many more
•Order Confirmation
•Cross-selling and Upselling
•Account Updates
•Loyalty Program offers
•Special Offers and Promotions
•Customer Verification, 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:
•
Speaker-wise Segmentation:
Time-coded segments for both agents and customers.
•
Non-Speech Labels:
Tags and labels for non-speech elements.
•
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 Retail domain call center conversational AI and ASR models for the Turkish language.
Metadata
The dataset provides comprehensive metadata for each conversation and participant:
•
Participant Metadata:
Unique identifier, age, gender, country, state, district, accent and dialect.
•
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 Turkish 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 Retail domain. Potential use cases include:
•
Speech Recognition Models:
Training and fine-tuning speech recognition models for Turkish.
•
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 Retail sector.
•
Smart Assistants and Chatbots:
Developing conversational agents and virtual assistants for customer service in the Retail industries.
•
Sentiment Analysis:
Analyzing customer sentiment and improving customer experience based on call center interactions.
•
Generative AI:
Training generative AI models capable of generating human-like responses, summaries, or content tailored to the Retail 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.
•
Environmental Conditions:
Custom collection in specific environmental conditions upon request.
•
Sample Rates:
Customizable from 8kHz to 48kHz.
•
Transcription Customization:
Tailored to specific guidelines and requirements.
License
This Retail 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.
Contact Us
Dataset Demographics
Language
Turkish
Language code
tr
Country
Turkey
Accents
Ağrı,...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
Start your AI/ML model creation journey with FutureBeeAI!
Contact Us
