English (US) Call Center Speech Dataset for Telecom

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

Speech training dataset for Telecom in English (India)
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About this Off-the-shelf Speech Dataset

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Introduction

Welcome to the US English 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 US English speakers from the FutureBeeAI Community.
  • Regions: Different states/provinces of United States of America, ensuring a balanced representation of US 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 US English 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 US English 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 US English.
  • 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)

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    00:00

    ATTRIBUTES

    TRANSCRIPTION

    TIME
    TRANSCRIPT
    0.629 - 1.657
    Hello Futurebee.
    3.137 - 4.022
    Hello Futurebee.
    7.439 - 12.361
    [filler] Hi I, I, I want to set up a landline for my business.
    12.662 - 12.916
    -
    12.928 - 14.230
    Whats you guys Spice (())
    15.455 - 17.317
    [filler] Hi thank you for calling Spice
    17.731 - 18.062
    point.
    18.533 - 20.344
    [filler] we are happy to take your call today.
    20.786 - 26.881
    [filler] thank you for asking about some pricing before. I give you that information. I just want to
    27.361 - 29.643
    ask a little bit of information from you.
    30.111 - 31.652
    [filler] Mr. whats your name?
    33.817 - 35.447
    <PII>Holp<initial>HOLP</initial></PII>
    34.612 - 36.585
    Okay Mr <PII>Holp</PII>. Got it.
    37.143 - 40.817
    Well nice to meet you. I am [filler] a representative (())
    41.664 - 46.155
    and I will be helping you today. So Mr. <PII>Holp</PII> what kind of business are you running?
    49.090 - 51.195
    [filler] I actually run a (()).
    51.902 - 58.448
    Okay (()) a business. And it sound like you will be having a store front for your landline. Is that correct?
    60.966 - 66.153
    Yeah that is correct [filler] and we kind of outgrown the idea of having a cell phone for the contact.
    66.691 - 68.602
    Got you. Okay I understand.
    69.111 - 71.278
    And how many employees do you had Mr.<PII>Hope</PII> ?
    73.602 - 74.587
    [filler] about fifty.
    74.712 - 81.402
    Fifty employees okay. So are you thinking of having just one landline [filler] receiver
    82.078 - 88.337
    or you thinking more receiver so that multiple employees can use that, the telephone at one time?
    90.581 - 93.331
    [filler] I am not sure what a receiver is.
    93.772 - 96.739
    but can I just tell you what I am trying to make happen [filler]?
    98.105 - 99.084
    Sure go ahead and tell me.
    99.706 - 103.176
    Okay so I want to have one
    104.391 - 107.766
    phone line like one number but multiple extensions.
    108.715 - 111.656
    [filler] and then of course each person can (()).
    112.153 - 117.551
    someone call someone else if necessary and I want to have that four of those lines available.
    119.557 - 125.061
    Perfect, Perfect, So when it comes to having the different extensions
    125.504 - 128.841
    each, each person can have their own unit
    129.615 - 133.532
    where you know thats what the actual phone is where you pick up the phone.
    134.002 - 137.693
    That would be the receiver. So it sounds like you want four receivers.
    139.424 - 140.550
    And they, okay.
    139.788 - 140.717
    Yeah, yeah thats
    141.556 - 142.229
    -
    143.126 - 144.008
    Okay perfect.
    144.812 - 146.181
    And then you are looking to have
    146.687 - 150.537
    [filler] each one has their own extension. Is that correct?
    152.574 - 157.841
    Yeah and how difficult is it to add additional receivers or extensions
    158.211 - 158.860
    down the road?
    160.079 - 164.264
    Oh its extremely easy. We do it all the time. You know companys grow
    164.717 - 167.656
    [filler] sometimes they even expand to different building
    168.217 - 169.899
    [filler] or different location.
    170.550 - 172.913
    And if that does happen, they are still able to keep
    173.501 - 176.151
    [filler] keep everything under the same package.
    177.109 - 179.811
    And even have it under the same phone number

    Dataset Details

    Card Head Line

    Language

    English

    Language code

    en-us

    Country

    USA

    Accents

    Arizona, California ...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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