- F
Indian English Call Center Data for Healthcare AI
- futurebeeai.com
wavUpdated Aug 1, 2022
Share
Facebook
Twitter
EmailClick to copy linkLink copiedCiteFutureBee AI (2022). Indian English Call Center Data for Healthcare AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/healthcare-call-center-conversation-english-indiawavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Dataset funded byFutureBeeAIDescriptionIntroduction
This Indian 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 Indian English speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.
•Participant Diversity:•Speakers: 60 verified native Indian English speakers from our contributor community.•Regions: Diverse provinces across India to ensure broad dialectal representation.•Participant Profile: Age range of 18–70 with a gender mix of 60% male and 40% female.•RecordingDetails:•Conversation Nature: Naturally flowing, unscripted conversations.•Call Duration: Each session ranges between 5 to 15 minutes.•Audio Format: WAV format, stereo, 16-bit depth at 8kHz and 16kHz sample rates.•Recording Environment: Captured in clear conditions without background noise or echo.Topic Diversity
The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).
•Inbound Calls:•Appointment Scheduling•New Patient Registration•Surgical Consultation•Dietary Advice and Consultations•Insurance Coverage Inquiries•Follow-up Treatment Requests, and more•OutboundCalls:•Appointment Reminders•Preventive Care Campaigns•Test Results & Lab Reports•Health Risk Assessment Calls•Vaccination Updates•Wellness Subscription Outreach, and moreThese 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.
•Transcription Includes:•Speaker-identified Dialogues•Time-coded Segments•Non-speech Annotations (e.g., silence, cough)•High transcription accuracy with word error rate is below 5%, backed by dual-layer QA checks.Metadata
Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.
•Participant Metadata: ID, gender, age, region, accent, and dialect.•Conversation Metadata: Topic, sentiment, call type, sample rate, and technical specs.Usage and Applications
This dataset can be used across a range of healthcare and voice AI use cases:
•<b - F
American English Call Center Data for Healthcare AI
- futurebeeai.com
wavUpdated Aug 1, 2022ShareFacebookTwitterEmailClick to copy linkLink copiedCiteFutureBee AI (2022). American English Call Center Data for Healthcare AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/healthcare-call-center-conversation-english-usawavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Area coveredUnited StatesDataset funded byFutureBeeAIDescriptionIntroduction
This US 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 US English speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.
•Participant Diversity:•Speakers: 60 verified native US English speakers from our contributor community.•Regions: Diverse provinces across United States of America to ensure broad dialectal representation.•Participant Profile: Age range of 18–70 with a gender mix of 60% male and 40% female.•RecordingDetails:•Conversation Nature: Naturally flowing, unscripted conversations.•Call Duration: Each session ranges between 5 to 15 minutes.•Audio Format: WAV format, stereo, 16-bit depth at 8kHz and 16kHz sample rates.•Recording Environment: Captured in clear conditions without background noise or echo.Topic Diversity
The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).
•Inbound Calls:•Appointment Scheduling•New Patient Registration•Surgical Consultation•Dietary Advice and Consultations•Insurance Coverage Inquiries•Follow-up Treatment Requests, and more•OutboundCalls:•Appointment Reminders•Preventive Care Campaigns•Test Results & Lab Reports•Health Risk Assessment Calls•Vaccination Updates•Wellness Subscription Outreach, and moreThese 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.
•Transcription Includes:•Speaker-identified Dialogues•Time-coded Segments•Non-speech Annotations (e.g., silence, cough)•High transcription accuracy with word error rate is below 5%, backed by dual-layer QA checks.Metadata
Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.
•Participant Metadata: ID, gender, age, region, accent, and dialect.•Conversation Metadata: Topic, sentiment, call type, sample rate, and technical specs.Usage and Applications
This dataset can be used across a range of healthcare and voice AI use cases:
• - F
British English Call Center Data for Healthcare AI
- futurebeeai.com
wavUpdated Aug 1, 2022ShareFacebookTwitterEmailClick to copy linkLink copiedCiteFutureBee AI (2022). British English Call Center Data for Healthcare AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/healthcare-call-center-conversation-english-ukwavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Area coveredUnited KingdomDataset funded byFutureBeeAIDescriptionIntroduction
This UK 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 UK English speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.
•Participant Diversity:•Speakers: 60 verified native UK English speakers from our contributor community.•Regions: Diverse provinces across United Kingdom to ensure broad dialectal representation.•Participant Profile: Age range of 18–70 with a gender mix of 60% male and 40% female.•RecordingDetails:•Conversation Nature: Naturally flowing, unscripted conversations.•Call Duration: Each session ranges between 5 to 15 minutes.•Audio Format: WAV format, stereo, 16-bit depth at 8kHz and 16kHz sample rates.•Recording Environment: Captured in clear conditions without background noise or echo.Topic Diversity
The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).
•Inbound Calls:•Appointment Scheduling•New Patient Registration•Surgical Consultation•Dietary Advice and Consultations•Insurance Coverage Inquiries•Follow-up Treatment Requests, and more•OutboundCalls:•Appointment Reminders•Preventive Care Campaigns•Test Results & Lab Reports•Health Risk Assessment Calls•Vaccination Updates•Wellness Subscription Outreach, and moreThese 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.
•Transcription Includes:•Speaker-identified Dialogues•Time-coded Segments•Non-speech Annotations (e.g., silence, cough)•High transcription accuracy with word error rate is below 5%, backed by dual-layer QA checks.Metadata
Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.
•Participant Metadata: ID, gender, age, region, accent, and dialect.•Conversation Metadata: Topic, sentiment, call type, sample rate, and technical specs.Usage and Applications
This dataset can be used across a range of healthcare and voice AI use cases:
•<b - F
Canadian English Call Center Data for Healthcare AI
- futurebeeai.com
wavUpdated Aug 1, 2022ShareFacebookTwitterEmailClick to copy linkLink copiedCiteFutureBee AI (2022). Canadian English Call Center Data for Healthcare AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/healthcare-call-center-conversation-english-canadawavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Area coveredCanadaDataset funded byFutureBeeAIDescriptionIntroduction
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.
•Participant Diversity:•Speakers: 60 verified native Canadian English speakers from our contributor community.•Regions: Diverse provinces across Canada to ensure broad dialectal representation.•Participant Profile: Age range of 18–70 with a gender mix of 60% male and 40% female.•RecordingDetails:•Conversation Nature: Naturally flowing, unscripted conversations.•Call Duration: Each session ranges between 5 to 15 minutes.•Audio Format: WAV format, stereo, 16-bit depth at 8kHz and 16kHz sample rates.•Recording Environment: Captured in clear conditions without background noise or echo.Topic Diversity
The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).
•Inbound Calls:•Appointment Scheduling•New Patient Registration•Surgical Consultation•Dietary Advice and Consultations•Insurance Coverage Inquiries•Follow-up Treatment Requests, and more•OutboundCalls:•Appointment Reminders•Preventive Care Campaigns•Test Results & Lab Reports•Health Risk Assessment Calls•Vaccination Updates•Wellness Subscription Outreach, and moreThese 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.
•Transcription Includes:•Speaker-identified Dialogues•Time-coded Segments•Non-speech Annotations (e.g., silence, cough)•High transcription accuracy with word error rate is below 5%, backed by dual-layer QA checks.Metadata
Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.
•Participant Metadata: ID, gender, age, region, accent, and dialect.•Conversation Metadata: Topic, sentiment, call type, sample rate, and technical specs.Usage and Applications
This dataset can be used across a range of healthcare and voice AI use cases:
• - F
Hindi Call Center Data for Healthcare AI
- futurebeeai.com
wavUpdated Aug 1, 2022ShareFacebookTwitterEmailClick to copy linkLink copiedCiteFutureBee AI (2022). Hindi Call Center Data for Healthcare AI [Dataset]. https://www.futurebeeai.com/dataset/speech-dataset/healthcare-call-center-conversation-hindi-indiawavAvailable download formatsDataset updatedAug 1, 2022Dataset provided byFutureBeeAIAuthorsFutureBee AILicensehttps://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Dataset funded byFutureBeeAIDescriptionIntroduction
This Hindi Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Hindi 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 Hindi speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.
•Participant Diversity:•Speakers: 60 verified native Hindi speakers from our contributor community.•Regions: Diverse provinces across India to ensure broad dialectal representation.•Participant Profile: Age range of 18–70 with a gender mix of 60% male and 40% female.•RecordingDetails:•Conversation Nature: Naturally flowing, unscripted conversations.•Call Duration: Each session ranges between 5 to 15 minutes.•Audio Format: WAV format, stereo, 16-bit depth at 8kHz and 16kHz sample rates.•Recording Environment: Captured in clear conditions without background noise or echo.Topic Diversity
The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).
•Inbound Calls:•Appointment Scheduling•New Patient Registration•Surgical Consultation•Dietary Advice and Consultations•Insurance Coverage Inquiries•Follow-up Treatment Requests, and more•OutboundCalls:•Appointment Reminders•Preventive Care Campaigns•Test Results & Lab Reports•Health Risk Assessment Calls•Vaccination Updates•Wellness Subscription Outreach, and moreThese 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.
•Transcription Includes:•Speaker-identified Dialogues•Time-coded Segments•Non-speech Annotations (e.g., silence, cough)•High transcription accuracy with word error rate is below 5%, backed by dual-layer QA checks.Metadata
Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.
•Participant Metadata: ID, gender, age, region, accent, and dialect.•Conversation Metadata: Topic, sentiment, call type, sample rate, and technical specs.Usage and Applications
This dataset can be used across a range of healthcare and voice AI use cases:
•<b style="font-weight: 
Not seeing a result you expected?
Learn how you can add new datasets to our index.
FacebookTwitterEmailIndian English Call Center Data for Healthcare AI
Indian English call center speech corpus in healthcare industry
https://www.futurebeeai.com/policies/ai-data-license-agreementhttps://www.futurebeeai.com/policies/ai-data-license-agreement
Introduction
This Indian 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 Indian 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: