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 UK 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 UK 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:

Update Frequency: "An automated scan for dataset updates occurs every day at 3:45 a.m."

For up to date information on service requests please visit, https://city.milwaukee.gov/ucc .

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

From potholes, abandoned vehicles, high weeds on vacant lots, and curbside trash to faulty traffic signals, the City of Milwaukee's Unified Call Center (UCC) makes it easy to submit service requests to solve problems. The UCC also allows you to track your service requests. Each time you complete a service request online, you will be assigned a tracking number that you can use to see when a City of Milwaukee representative expects to investigate or take care of your request.

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.

This dataset captures monthly data from HSS' phone system and includes metrics pertaining to Calls Answered, Average Speed of Answer, Abandonment Rate, In-person Assistance. This data supports the City's Performance Measures requirements. In April of 2023 HSS switched to a new phone system - WEBEX (Finess).

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

Introduction

This Vietnamese 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 Vietnamese-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 Vietnamese 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:

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Introduction

This Mexican Spanish 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 Spanish-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 Mexican Spanish 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:

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Home Boston English DatasetHigh-Quality Boston English Call-Center, General Conversation, and Podcast Dataset for AI & Speech Models Contact Us OverviewTitle (Language)Boston English Language DatasetDataset TypesCall Center, General Conversation, Media Data…

Call Center Transcripts Dataset

Overview

This dataset contains transcripts of call center interactions, capturing a variety of customer service scenarios. It includes details such as call type, sentiment, customer name, order number, product number, and the full conversation transcript.

Data Description

The dataset is structured in a CSV format. Here's a breakdown of the columns:

Column

Type

Description

This data set contains service request data from the 311 call center in Kansas City, MO. In March 2021, Kansas City began transitioning to a new record management system for 311 requests, and has created a new dataset published under the name 311 Call Center Reported Issues: https://data.kcmo.org/311/311-Call-Center-Reported-Issues/d4px-6rwg.

This dataset will continue to be updated as departments close service requests in the previous system and will be maintained as an archive dataset after the system is no longer in use.

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