Hindi (India) Call Center Speech Dataset for Telecom

The audio dataset comprises call center conversations for the Telecom domain, featuring native Hindi speakers from India. 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

AI speech dataset for Telecom in French (France)
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About this Off-the-shelf Speech Dataset

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Introduction

Welcome to the Hindi Call Center Speech Dataset for the Telecom domain designed to enhance the development of call center speech recognition models specifically for the Telecom 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 Telecom domain, designed to build robust and accurate customer service speech technology.

  • •Participant Diversity:
  • •
    Speakers: 60 expert native Hindi speakers from the FutureBeeAI Community.
  • •
    Regions: Different states/provinces of India, ensuring a balanced representation of Hindi 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:
  • •Phone Number Porting
  • •Network Connectivity Issues
  • •Billing and Payments
  • •Technical Support
  • •Service Activation
  • •International Roaming Enquiry
  • •Refunds and Billing Adjustments
  • •Emergency Service Access, and many more
  • •Outbound Calls:
  • •Welcome Calls / Onboarding Process
  • •Payment Reminders
  • •Customer Surveys
  • •Technical Updates
  • •Service Usage Reviews
  • •Network Compliant Status Call, 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 Telecom domain call center conversational AI and ASR models for the Hindi 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 Hindi 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 Telecom domain. Potential use cases include:

  • •
    Speech Recognition Models: Training and fine-tuning speech recognition models for Hindi.
  • •
    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 Telecom sector.
  • •
    Smart Assistants and Chatbots: Developing conversational agents and virtual assistants for customer service in the Telecom 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 Telecom 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 Telecom 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)

    Card Head Line
    00:00

    ATTRIBUTES

    CHANNEL 1 :Female(26)
    CHANNEL 2 :Female(39)
    TOPIC :Telecom refunds and billing adjustment
    CALL TYPE :Inbound
    FORMAT :wav, json

    TRANSCRIPTION

    TIME
    TRANSCRIPT
    0.650 - 1.916
    <lang:Foreign>Hello Future Bee</lang:Foreign>
    3.275 - 4.425
    <lang:Foreign>Hello Future Bee</lang:Foreign>
    6.575 - 9.575
    क्या मेरी बात #अः <lang:Foreign>Jio Services</lang:Foreign> में हो रही है
    11.075 - 16.651
    <lang:Foreign>Yes ma'am</lang:Foreign> आपका <lang:Foreign>Connection Jio Service</lang:Foreign> में लगा हुआ है <lang:Foreign>ma'am</lang:Foreign> कैसे में आपकी सहायता कर सकती हूँ <lang:Foreign>ma'am</lang:Foreign>
    18.976 - 25.576
    <lang:Foreign>ma'am</lang:Foreign> मैं, आपसे अपने <lang:Foreign>Jio Fiber</lang:Foreign> के <lang:Foreign>Refund</lang:Foreign> के बारे में जानकारी चाहती हूँ मुझे अपना
    26.201 - 30.651
    <lang:Foreign>Jio Fiber</lang:Foreign> बंद करना हैं तो मैं जानना चाहती हूँ की अगर मुझे उसका
    31.226 - 34.201
    <lang:Foreign>Refund</lang:Foreign> मिल जायेगा, क्योंकी मेरा पैक <lang:Foreign>Already Recharged</lang:Foreign> है।
    35.601 - 41.452
    <lang:Foreign>Okay ma'am</lang:Foreign> #अः क्षमा चाहती हूँ हमारी तरफ से आपको ये दिक्कत #अः
    42.402 - 46.524
    <lang:Foreign>Face</lang:Foreign> करनी पड़ रही है #अः मैं आपका पूरा नाम जान सकती हूँ <lang:Foreign>Madam</lang:Foreign>
    48.427 - 50.427
    जी <PII>प्रियंका तिवारी</PII>
    50.677 - 51.602
    धन्यवाद <lang:Foreign>ma'am</lang:Foreign>
    51.702 - 55.027
    आप कौन से <lang:Foreign>Area</lang:Foreign> और आपका क्या <lang:Foreign>Address</lang:Foreign> है <lang:Foreign>ma'am</lang:Foreign>
    58.002 - 60.277
    जी मैं <lang:Foreign>Civil Lines</lang:Foreign> से बात कर रही हूँ
    60.702 - 63.678
    मैं रतन <lang:Foreign>Apartment</lang:Foreign> से बात कर रही हूँ <lang:Foreign>Civil Lines</lang:Foreign>
    65.528 - 67.478
    <lang:Foreign>Okay ma'am</lang:Foreign> और आपका <lang:Foreign>PIN Code</lang:Foreign> क्या है <lang:Foreign>ma'am</lang:Foreign>
    69.603 - 70.903
    <lang:Foreign><PII>Two Zero Nine</PII></lang:Foreign>
    71.003 - 71.503
    #हम्म
    72.778 - 73.803
    <lang:Foreign><PII>Eight Double Zero</PII></lang:Foreign>
    75.403 - 75.828
    #हम्म
    76.803 - 77.203
    <lang:Foreign><PII>One</PII></lang:Foreign>
    77.328 - 87.929
    <lang:Foreign>Okay ma'am</lang:Foreign>धन्यवाद आपके <lang:Foreign>Details</lang:Foreign> देने के लिए #अः <lang:Foreign>ma'am</lang:Foreign> आपका <lang:Foreign>Jio Fiber</lang:Foreign> का <lang:Foreign>Connection</lang:Foreign> है आपके घर पे उसका मेरेको <lang:Foreign>Ten digit</lang:Foreign> का <lang:Foreign>Number Provide</lang:Foreign> कीजिये <lang:Foreign>ma'am</lang:Foreign>
    90.879 - 91.454
    <lang:Foreign>Okay</lang:Foreign>
    92.654 - 93.504
    <lang:Foreign>Note</lang:Foreign> करिए आप।
    93.979 - 95.154
    हाँ <lang:Foreign>ma'am</lang:Foreign> बोलिए
    97.404 - 98.404
    <lang:Foreign><PII>Seven Double Zero</PII></lang:Foreign>
    99.129 - 99.479
    हाँ <lang:Foreign>ma'am</lang:Foreign>
    101.479 - 102.504
    <lang:Foreign><PII>Eight Six Nine</PII></lang:Foreign>
    101.779 - 102.404
    हाँ <lang:Foreign>ma'am</lang:Foreign>
    104.554 - 105.279
    <lang:Foreign><PII>two three one</PII></lang:Foreign>
    105.304 - 105.954
    <lang:Foreign>Okay</lang:Foreign>
    107.429 - 107.879
    <lang:Foreign><PII>zero</PII></lang:Foreign>
    107.604 - 108.254
    धन्यवाद <lang:Foreign>ma'am</lang:Foreign>
    109.704 - 110.505
    #अः
    110.905 - 120.055
    जैसे की हम देख पा रहे हैं हमारे <lang:Foreign>System</lang:Foreign> में आपका ये <lang:Foreign>Services</lang:Foreign> अभी चालू है आपको इसका <lang:Foreign>Refund</lang:Foreign> के बारे में पूछना हैं <lang:Foreign>Right ma'am</lang:Foreign>
    122.280 - 133.530
    अच्छा ठीक ये #अः हमारे पे ऐसा है की अगर आप <lang:Foreign>Services Discontinue</lang:Foreign> करते हो अगर बंद करते हो #अः <lang:Foreign>thirty Days</lang:Foreign> के <lang:Foreign>Trial Period</lang:Foreign> में
    123.530 - 124.005
    जी।
    133.630 - 142.556
    तो आपको <lang:Foreign>ma'am</lang:Foreign> हमारे जो <lang:Foreign>Install</lang:Foreign> करके गए हैं <lang:Foreign>Devices <initial>WIFI</initial> Router</lang:Foreign> और एक जो <lang:Foreign>Device</lang:Foreign> है
    142.731 - 148.492
    वो आपको <lang:Foreign>Working Condition</lang:Foreign> में आपको <lang:Foreign>return</lang:Foreign> करना पड़ेगा आपके जो
    148.606 - 153.269
    नजदीक के जो भी <lang:Foreign>Jio Store</lang:Foreign> में जा के आप वो <lang:Foreign>Return</lang:Foreign> कर सकती हो
    153.697 - 160.457
    तो आपका जो भी #अः <lang:Foreign>Fees</lang:Foreign> है <lang:Foreign>Security Deposit</lang:Foreign> वो आपको पूरा <lang:Foreign>Refund</lang:Foreign> मिल जाएगा<lang:Foreign>Madam</lang:Foreign>
    164.535 - 165.196
    <lang:Foreign>okay</lang:Foreign>
    165.550 - 172.107
    तो मतलब मेरा <lang:Foreign>Security Deposit ,Refund</lang:Foreign>हो जायेगा पर मेरा, जो<lang:Foreign>Recharge</lang:Foreign> है उसका कोई <lang:Foreign>Refund</lang:Foreign> नहीं हो सकता
    171.407 - 178.457
    वो <lang:Foreign>Recharge Madam</lang:Foreign> वो आपका कैसे वो<lang:Foreign>Bill</lang:Foreign>#अः<lang:Foreign>Billing Cycle</lang:Foreign> में चला गया हुआ है जो आपने ये
    178.507 - 189.208
    <lang:Foreign>Days</lang:Foreign> का <lang:Foreign>utilise</lang:Foreign> किया हुआ है तो फिर बाद में आपका जो <lang:Foreign>refund</lang:Foreign> है वो आपके <lang:Foreign>Billing</lang:Foreign> के इसमें हम लोग <lang:Foreign>Adjustment</lang:Foreign> कर देंगे <lang:Foreign>Madam</lang:Foreign>
    192.158 - 197.258
    <lang:Foreign>Okay</lang:Foreign> तो मेरा ये, जो <lang:Foreign>Recharge</lang:Foreign> है, वो वहाँ पर <lang:Foreign>Amount Deduct</lang:Foreign>हो जायेगा।
    197.758 - 206.674
    हाँ वो हम लोग <lang:Foreign>Deduct</lang:Foreign> कर देंगे और जो भी आपका फ़िर <lang:Foreign>Balance</lang:Foreign> बनता है तो वो आपको ऊपर से आपको उपर से आपको <lang:Foreign>pay</lang:Foreign> पे करना पड़ेगा <lang:Foreign>Madam</lang:Foreign>
    210.034 - 210.609
    <lang:Foreign>Okay</lang:Foreign>
    211.584 - 214.609
    तो इसके लिए मुझे मतलब क्या, <lang:Foreign>Process follow</lang:Foreign> करनी पड़ेगी।
    215.784 - 226.279
    आप आपके जो भी<lang:Foreign>Nearby Jio Store</lang:Foreign> है आप जाइए वहां पे आपका आधार कार्ड लेकर जाइए और जो आपके पास <lang:Foreign>Devices</lang:Foreign> हैं हमारे
    226.434 - 239.110
    वो आप लेकर जाइए और उनको बता दीजिए आपका जो <lang:Foreign>Number</lang:Foreign> है <lang:Foreign>ten digit</lang:Foreign>का और वो उनके <lang:Foreign>System</lang:Foreign> में पूरा<lang:Foreign>Detail</lang:Foreign> में <lang:Foreign>Check</lang:Foreign> करेंगे और वो आपको पूरा <lang:Foreign>Billing</lang:Foreign> का
    239.360 - 253.460
    <lang:Foreign>Cycle</lang:Foreign> और पूरा <lang:Foreign>Bill</lang:Foreign>आपको दे देंगे और उस पे वो लोग आपका जो भी #अः <lang:Foreign>Refund</lang:Foreign>है <lang:Foreign>Service Deposit</lang:Foreign> का वो <lang:Foreign>Initiate</lang:Foreign> करके जो भी <lang:Foreign>Balance</lang:Foreign> दिखता है, वो आपको पूरा बता देंगे <lang:Foreign>Madam</lang:Foreign> वहाँ पे
    256.385 - 261.536
    <lang:Foreign>Okay ma'am</lang:Foreign> तो ये मुझे खुद से <lang:Foreign>Disconnect</lang:Foreign> करवाना पड़ेगा या फिर आप कोई <lang:Foreign>Employee</lang:Foreign> भेजेंगी
    262.133 - 266.636
    #अः नहीं<lang:Foreign>Madam</lang:Foreign> आपको<lang:Foreign>Nearby Store</lang:Foreign>में जाकर ही वो करना पड़ेगा <lang:Foreign>Madam</lang:Foreign>
    271.786 - 274.936
    ठीक है फिर जानकारी देने के लिए आपका
    275.686 - 276.411
    शुक्रिया
    277.511 - 279.361
    <lang:Foreign>Thankyou Madam</lang:Foreign> आपका दिन शुभ रहे
    282.462 - 283.212
    <lang:Foreign>Okay</lang:Foreign>
    282.812 - 284.587
    और कुछ <lang:Foreign>Questions</lang:Foreign> है मैडम आपका
    286.862 - 290.612
    जी नहीं,फिलहाल के लिए तो नहीं मैं आगे आपको
    291.062 - 297.862
    संपर्क कर लूंगी अगर मुझे कोई ज़रूरत पड़ी,अभी मैं<lang:Foreign>Disconnect</lang:Foreign> करके अपने नजदीकी <lang:Foreign>Customer Care</lang:Foreign> में जाती हूँ
    299.012 - 300.512
    <lang:Foreign>Okay ma'am, Thankyou Madam</lang:Foreign>

    Dataset Details

    Card Head Line

    Language

    Hindi

    Language code

    hi-in

    Country

    India

    Accents

    Andhra Pradesh, Arunachal Pradesh ...more

    Gender Distribution

    M:60, F:40

    Age Group

    18-70

    File Details

    Card Head Line

    Environment

    Silent, Noisy

    Bit Depth

    16 bit

    Format

    wav

    Sample rate

    8khz & 16 khz

    Channel

    Stereo

    Audio file duration

    5-15 minutes

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