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

About Gradiet Line

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.

  • Participant Diversity:
  • 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.
  • Recording Details:
  • 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.

  • Inbound Calls:
  • 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
  • Outbound Calls:
  • 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.

  • Customization & Custom Collection Options:
  • 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

    Use of speech data in Conversational AI

    Call Center Conversational AI

    Use of speech data for Automatic Speech Recognition

    ASR

    Use of speech data for Chatbot & voicebot creation

    Chatbot

    Use of speech data in Language Modeling

    Language Modelling

    Use of speech data in Text-into-speech

    TTS

    Speech data usecase in Speech Analytics

    Speech Analytics

    Dataset Sample(s)

    Sample Line

    ATTRIBUTES

    Channel 1Channel 2Format
    Female(24)Male(24)wav, json

    TRANSCRIPTION

    LABELSTARTENDCHANNELTRANSCRIPT
    Speech0.2701.62693367119Bonjour Future Bee
    Speech2.0926.34593367119Bienvenue au service client, comment puis-je vous aider aujourd'hui ?
    Speech6.86812.70187236739Bonjour, je suis Richard Dupond, et je suis un peu inquiet sur une transaction sur mon compte bancaire
    Speech14.08621.66193367119Je comprends, Monsieur Dupont, pouvons-nous commencer par votre numéro de compte ou votre nom complet pour que je puisse accéder à votre dossier ?
    Speech23.67226.75387236739Oui bien sûr, mon nom complet est Richard Dupond
    Speech27.15530.32893367119D'accord, Richard Dupond, avec un T ou avec un D ?
    Speech31.40232.18487236739avec un D
    Speech32.44333.47793367119Et mon numéro de compte c'est cent vingt-trois
    Speech32.82235.66187236739D'accord
    Speech36.81638.16793367119Alors, cent vingt-trois
    Speech38.55739.66187236739quatre cents cinquante six
    Speech40.27042.16793367119quatre cents cinquante six
    Speech43.19544.28787236739huit cent cinquante neuf
    Speech44.66746.54093367119huit cent cinquante neuf
    Speech47.27052.09293367119Pouvez-vous m'indiquer la date et le montant de la transaction en question ?
    Speech53.58657.22487236739Oui donc c'est une transaction qui a eu lieu hier pour un montant de cinq cent euros
    Speech57.63860.94387236739Mais je me souviens pas l'avoir initié, je ne pense pas que ce soit moi
    Speech61.36863.97793367119Donc une transaction de cinq cents euros
    Speech64.36870.20793367119Et hier d'accord, donc a priori voilà personne n'a accès à vos comptes ?
    Speech64.83965.06387236739#Mmm
    Speech72.38573.54093367119D'accord, compris
    Speech72.92573.21387236739non
    Speech74.14976.28793367119Permettez-moi de vérifier cela dans votre système

    TRANSCRIPTION

    TIMETRANSCRIPT
    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

    Details Headline

    Language

    French

    Language code

    fr

    Country

    France

    Accents

    Français méridional,...more

    Gender Distribution

    M:60, F:40

    Age Group

    18-70

    Audio File Details

    Details Headline

    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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