Update Frequency: N/A

A log of the Unified Call Center's service requests.

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

Introduction

This US English Call Center Speech Dataset for the Telecom industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for English-speaking telecom customers. Featuring over 30 hours of real-world, unscripted audio, it delivers authentic customer-agent interactions across key telecom support scenarios to help train robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI engineers, telecom automation teams, and NLP researchers to build high-accuracy, production-ready models for telecom-specific use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native US English speakers. Captured in realistic customer support settings, these conversations span a wide range of telecom topics from network complaints to billing issues, offering a strong foundation for training and evaluating telecom voice AI solutions.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral ensuring broad scenario coverage for telecom AI development.

This variety helps train telecom-specific models to manage real-world customer interactions and understand context-specific voice patterns.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, allowing for faster development of ASR and conversational AI systems in the Telecom domain.

Metadata

Rich metadata is available for each participant and conversation:

Introduction

This Hindi Call Center Speech Dataset for the BFSI (Banking, Financial Services, and Insurance) sector is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for Hindi-speaking customers. Featuring over 30 hours of real-world, unscripted audio, it offers authentic customer-agent interactions across a range of BFSI services to train robust and domain-aware ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI developers, financial technology teams, and NLP researchers to build high-accuracy, production-ready models across BFSI customer service scenarios.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native Hindi speakers. Captured in realistic financial support settings, these conversations span diverse BFSI topics from loan enquiries and card disputes to insurance claims and investment options, providing deep contextual coverage for model training and evaluation.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral, ensuring real-world BFSI voice coverage.

This variety ensures models trained on the dataset are equipped to handle complex financial dialogues with contextual accuracy.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, making financial domain model training faster and more accurate.

Metadata

Rich metadata is available for each participant and conversation:

Explore a rich Arabic customer speech dataset designed for call center customer care applications. Perfect for training AI models and enhancing customer service solutions.

Discover our English call center dataset designed for phone service support. Perfect for speech analysis, AI training, and upgrading customer service systems.

Introduction

This Canadian English Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of English speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Canadian English speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

Explore a rich Call Center Customer Speech Dataset in English designed for customer care insights, improving service quality, and enhancing customer experiences.

Comprehensive dataset of 2,062 Call centers in United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 172 Call centers in California, United States as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

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Call Center Outsourcing Market Size 2025-2029

The call center outsourcing market size is forecast to increase by USD 26.3 billion, at a CAGR of 4.3% between 2024 and 2029.

The market is experiencing significant shifts, driven by the emergence of new destinations in emerging economies and the increasing trend of mergers and acquisitions among companies. These developments offer both opportunities and challenges for market participants. Emerging countries, such as India, the Philippines, and Eastern European nations, are becoming increasingly popular call center destinations due to their large, English-speaking workforces and cost advantages. This trend is expected to continue, as companies seek to reduce costs and improve operational efficiency. However, the increasing cost of call center outsourcing services poses a challenge for businesses. Companies are facing rising labor costs, particularly in traditional outsourcing destinations like India, and are passing these costs onto their clients.
Additionally, the complexity of managing multicompany environments and ensuring data security is becoming more challenging as companies outsource to multiple companies. To capitalize on opportunities and navigate challenges effectively, companies should focus on building strategic partnerships with companies and exploring new outsourcing destinations. They should also invest in technology solutions to streamline operations and improve customer experience. By staying informed of market trends and adapting to changing market dynamics, companies can effectively leverage call center outsourcing to achieve operational efficiency and cost savings.

What will be the Size of the Call Center Outsourcing Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, shaped by dynamic market conditions and diverse applications across various sectors. Speech recognition technology enhances customer interactions, while recruitment and hiring processes adapt to the growing demand for work-from-home agents. Call center security remains a top priority, with advanced measures ensuring data privacy and compliance with regulations. Agent retention strategies are refined through continuous training and performance management, integrating reporting and dashboards, quality assurance, and predictive analytics. Virtual call centers expand operations, offering scalability and capacity, while multi-site call centers leverage business intelligence for improved performance metrics. Disaster recovery plans and network connectivity ensure business continuity, as call center infrastructure adapts to the demands of onshore, nearshore, and offshore outsourcing.

Call monitoring and agent training are crucial components of ongoing improvement, with hardware and software solutions enhancing overall efficiency. Predictive analytics and data visualization provide valuable insights, enabling proactive decision-making and enhancing customer satisfaction. Call routing optimizes operations, and compliance regulations guide the industry's evolution, shaping the call center landscape.

How is this Call Center Outsourcing Industry segmented?

The call center outsourcing industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  IT and telecom
  BFSI
  Healthcare
  Retail
  Others


Type

  Technical Support Services
  Customer Support Services
  Telemarketing Services
  Others


Deployment

  Onshore Outsourcing
  Offshore Outsourcing
  Nearshore Outsourcing


Organization Size

  Small and Medium Enterprises (SMEs)
  Large Enterprises


Geography

  North America

    US
    Canada


  Europe

    Germany
    Italy
    Poland
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Philippines
    Vietnam


  South America

    Argentina
    Brazil


  Rest of World (ROW)

.

By End-user Insights

The it and telecom segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth, particularly in the IT and telecom sectors. Technological advancements and the increasing demand for digital communication and content are driving this expansion. The telecom services industry is poised for steady growth with the emergence of 5G technology. Numerous telecom companies are investing in 5G infrastructure worldwide, with estimates suggesting there will be over 3.6 billion 5G connections by 2025. Call recording, agent performance management, reporting and dashboards, quality assurance, nearshore and onshore outsourcing, agent trainin

Comprehensive dataset of 1,151 Call centers in Germany as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

In 20223 there was a slight increase in the number of call center jobs created in several countries and regions. In the Philippines, for example, there were approximately ****** more call center jobs created than in 2022. India created the highest number of jobs globally within this industry, approximately ****** more when compared to the previous year. In 2022, the region that created the most call center jobs in the United States was in the *********.

Comprehensive dataset of 442 Call centers in United Kingdom as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This dataset shows the calls received by the consumer contact centre by date, type of inquiry (call code), self-reported postal code and constituency.

Introduction

This French Call Center Speech Dataset for the Telecom industry is purpose-built to accelerate the development of speech recognition, spoken language understanding, and conversational AI systems tailored for French-speaking telecom customers. Featuring over 30 hours of real-world, unscripted audio, it delivers authentic customer-agent interactions across key telecom support scenarios to help train robust ASR models.

Curated by FutureBeeAI, this dataset empowers voice AI engineers, telecom automation teams, and NLP researchers to build high-accuracy, production-ready models for telecom-specific use cases.

Speech Data

The dataset contains 30 hours of dual-channel call center recordings between native French speakers. Captured in realistic customer support settings, these conversations span a wide range of telecom topics from network complaints to billing issues, offering a strong foundation for training and evaluating telecom voice AI solutions.

Topic Diversity

This speech corpus includes both inbound and outbound calls with varied conversational outcomes like positive, negative, and neutral ensuring broad scenario coverage for telecom AI development.

This variety helps train telecom-specific models to manage real-world customer interactions and understand context-specific voice patterns.

Transcription

All audio files are accompanied by manually curated, time-coded verbatim transcriptions in JSON format.

These transcriptions are production-ready, allowing for faster development of ASR and conversational AI systems in the Telecom domain.

Metadata

Rich metadata is available for each participant and conversation:

Comprehensive dataset of 809 Call centers in Colombia as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 5 Call centers in Yamaguchi, Japan as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

This dataset contains results from 311 customer surveys. Someone who calls 311 for an issue is sent a small survey after the City believes it has addressed the issue. Not everyone is surveyed, due to some calls being anonymous, or not being able to locate the requester's mailing address.

 Results are provided on a 1-5 scale. 1 is unacceptable, 2 is poor, 3 is acceptable, 4 is good, 5 is excellent. 

 Because the cards are physically mailed out there is a time delay between when a service request is closed and when the City is able to enter the survey results into our system. This data set refreshed daily. 

 Multiple results per 311 case are possible due to multiple people requesting the same service for the same location. For example, if 10 people ask 311 to have the City repaint a crosswalk at 12th and Grand Street, each of them will be mailed a survey and the results will show in this dataset.

Europe had the largest call center market in 2017, generating around ** billion U.S. dollars in revenue, followed by North America, with ** billion U.S. dollars. Latin America had the smallest market in that year, with ** billion U.S. dollars in revenue. Call center market The call center market includes the section of an organization that provides assistance to customers by phone. This can be for existing customers, for example by answering queries about the product or service they purchased, or for sales-based activities to obtain new customers. Given the broad nature of these services, virtually every industry is represented in the call center market, making it a prime candidate for outsourcing. Outsourcing can achieve lower costs through locating call center infrastructure in countries with lower costs, such as India and the Philippines, and significantly reduce the capital expenditure required to set up a call center. This has led to a growing outsourced call center market that is expected to reach **** billion U.S. dollars by 2020. Overall market growth Some analysts expect the overall call center market to experience strong growth in coming years, predicting it will more than double in size by 2022. However, other analysts expect growth to be more limited and unevenly spread. For example, some predict the European market to shrink in size by 2025, while the United States will grow to be the largest market. Data from the last few years seems to support the hypothesis that the U.S. market will overtake Europe, with many more new call centers opening there between 2016 and 2018.

Raw data for project: Gel polymer electrolytes with biodegradable matrix for lithium-ion and sodium-ion batteries - funded by the Naional Science Center under the call MINIATURA-6 (Grant No 2022/06/X/ST5/00892).The data set contains the following:1) the raw SEM images (scanning electron miscroscope) the selected gel polymer electrolytes with modified polymer matrix (hybrid matrix made of synthetic polymer and biopolymer in different mass ratios - given in the dataset). Magnitude of 5000x, accelerating voltage 10 kV. Data in tif. format. Related to publication: A. Gabryelczyk, A. Swiderska-Mocek, Tailoring the Properties of Gel Polymer Electrolytes for Sodium-Ion Batteries Using Ionic Liquids: A Review, Chemistry - A European Journal, 30(27), (2024) 2202304207.2) the raw results of thermogravimetric analysis (TGA) of the selected gel polymer electrolytes with modified polymer matrix (hybrid matrix made of synthetic polymer and biopolymer in different mass ratios - given in the dataset). Data in xml. format.Temperature range of the measurement: 30-700 °CTemperature step: 10 °C/min.Atmosphere: nitrogen gas flow, 250 mL/min.Sample mass: 9-14 mg3) the raw project-related data to the manuscript entitled "Biodegradable hybrid polymer matrix based on starch for gel polymer electrolytes – exploring alternatives for sustainable sodium-ion batteries".The data set contains unprocessed results related to the following experiments: biodegradability records, cycling performance of Na-based battery, dimensional shrinkage test, differential scanning calorimetry (DSC) of the gel polymer electrolytes, electrochemical window of the gel polymer electrolytes, Ionic conductivity of the gel polymer electrolytes, SEM images of the gel polymer electrolytes, and TGA of the gel polymer electrolytes (operating conditions are in each specific file in the zip. folder).