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French (France) Call Center Speech Dataset for BFSI
The audio dataset comprises call center conversations for the BFSI domain, featuring native French speakers from France. 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 French Call Center Speech Dataset for the BFSI domain designed to enhance the development of call center speech recognition models specifically for the BFSI 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 BFSI domain, designed to build robust and accurate customer service speech technology.
•
Speakers:
60 People expert native French speakers from the FutureBeeAI Community.
•
Regions:
Different states/provinces of France, ensuring a balanced representation of French 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.
•Debit Card Block Request
•Home Loan Enquiry
•Transaction Disputes
•Credit Card Billing Dispute
•Account Closure Procedures
•Claim Procedures
•Premium Payments
•Policy Comparison
•Policy Cancellation or Lapse
•Insurance Renewal Options
•Retirement Planning
•Investment Risk Assessment Questionnaires
•Tax-efficient Investment Strategies
•Investment Performance Enquiry, and many more
•Credit Card Offers
•Loan Offers
•Loyalty Program Benefits
•Customer Satisfaction Surveys
•EMI Reminder Call
•Policy Upgrade Offers
•Claim Status Updates
•Policyholder Loyalty Benefits
•Insurance Policyholder Surveys
•Term Life Insurance Offer
•Investment Opportunities
•Retirement Savings Review, 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 BFSI domain call center conversational AI and ASR models for the French 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 French 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 BFSI domain. Potential use cases include:
•
Speech Recognition Models:
Training and fine-tuning speech recognition models for French.
•
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 BFSI sector.
•
Smart Assistants and Chatbots:
Developing conversational agents and virtual assistants for customer service in the BFSI 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 BFSI 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 BFSI 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)
ATTRIBUTES
| Channel 1 | Channel 2 | Format |
|---|---|---|
| Female(24) | Male(24) | wav, json |
TRANSCRIPTION
| LABEL | START | END | CHANNEL | TRANSCRIPT |
|---|---|---|---|---|
| Speech | 0.270 | 1.626 | 93367119 | Bonjour Future Bee |
| Speech | 2.092 | 6.345 | 93367119 | Bienvenue au service client, comment puis-je vous aider aujourd'hui ? |
| Speech | 6.868 | 12.701 | 87236739 | Bonjour, je suis Richard Dupond, et je suis un peu inquiet sur une transaction sur mon compte bancaire |
| Speech | 14.086 | 21.661 | 93367119 | Je comprends, Monsieur Dupont, pouvons-nous commencer par votre numéro de compte ou votre nom complet pour que je puisse accéder à votre dossier ? |
| Speech | 23.672 | 26.753 | 87236739 | Oui bien sûr, mon nom complet est Richard Dupond |
| Speech | 27.155 | 30.328 | 93367119 | D'accord, Richard Dupond, avec un T ou avec un D ? |
| Speech | 31.402 | 32.184 | 87236739 | avec un D |
| Speech | 32.443 | 33.477 | 93367119 | Et mon numéro de compte c'est cent vingt-trois |
| Speech | 32.822 | 35.661 | 87236739 | D'accord |
| Speech | 36.816 | 38.167 | 93367119 | Alors, cent vingt-trois |
| Speech | 38.557 | 39.661 | 87236739 | quatre cents cinquante six |
| Speech | 40.270 | 42.167 | 93367119 | quatre cents cinquante six |
| Speech | 43.195 | 44.287 | 87236739 | huit cent cinquante neuf |
| Speech | 44.667 | 46.540 | 93367119 | huit cent cinquante neuf |
| Speech | 47.270 | 52.092 | 93367119 | Pouvez-vous m'indiquer la date et le montant de la transaction en question ? |
| Speech | 53.586 | 57.224 | 87236739 | Oui donc c'est une transaction qui a eu lieu hier pour un montant de cinq cent euros |
| Speech | 57.638 | 60.943 | 87236739 | Mais je me souviens pas l'avoir initié, je ne pense pas que ce soit moi |
| Speech | 61.368 | 63.977 | 93367119 | Donc une transaction de cinq cents euros |
| Speech | 64.368 | 70.207 | 93367119 | Et hier d'accord, donc a priori voilà personne n'a accès à vos comptes ? |
| Speech | 64.839 | 65.063 | 87236739 | #Mmm |
| Speech | 72.385 | 73.540 | 93367119 | D'accord, compris |
| Speech | 72.925 | 73.213 | 87236739 | non |
| Speech | 74.149 | 76.287 | 93367119 | Permettez-moi de vérifier cela dans votre système |
TRANSCRIPTION
| TIME | TRANSCRIPT |
|---|---|
| 0.270 1.626 | Bonjour Future Bee |
| 2.092 6.345 | Bienvenue au service client, comment puis-je vous aider aujourd'hui ? |
| 6.868 12.701 | Bonjour, je suis Richard Dupond, et je suis un peu inquiet sur une transaction sur mon compte bancaire |
| 14.086 21.661 | Je comprends, Monsieur Dupont, pouvons-nous commencer par votre numéro de compte ou votre nom complet pour que je puisse accéder à votre dossier ? |
| 23.672 26.753 | Oui bien sûr, mon nom complet est Richard Dupond |
| 27.155 30.328 | D'accord, Richard Dupond, avec un T ou avec un D ? |
| 31.402 32.184 | avec un D |
| 32.443 33.477 | Et mon numéro de compte c'est cent vingt-trois |
| 32.822 35.661 | D'accord |
| 36.816 38.167 | Alors, cent vingt-trois |
| 38.557 39.661 | quatre cents cinquante six |
| 40.270 42.167 | quatre cents cinquante six |
| 43.195 44.287 | huit cent cinquante neuf |
| 44.667 46.540 | huit cent cinquante neuf |
| 47.270 52.092 | Pouvez-vous m'indiquer la date et le montant de la transaction en question ? |
| 53.586 57.224 | Oui donc c'est une transaction qui a eu lieu hier pour un montant de cinq cent euros |
| 57.638 60.943 | Mais je me souviens pas l'avoir initié, je ne pense pas que ce soit moi |
| 61.368 63.977 | Donc une transaction de cinq cents euros |
| 64.368 70.207 | Et hier d'accord, donc a priori voilà personne n'a accès à vos comptes ? |
| 64.839 65.063 | #Mmm |
| 72.385 73.540 | D'accord, compris |
| 72.925 73.213 | non |
| 74.149 76.287 | Permettez-moi de vérifier cela dans votre système |
Dataset Demographics
Language
French
Language code
fr
Country
France
Accents
Français méridional,...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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