English (US) Call Center Speech Dataset for BFSI

The audio dataset comprises call center conversations for the BFSI domain, featuring native English speakers from US. It includes speech data, detailed metadata and accurate transcriptions.

About this Off-the-shelf Speech Dataset

Introduction

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

•Participant Diversity:

•

Speakers: 60 People 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:

•Debit Card Block Request

•Home Loan Enquiry

•Transaction Disputes

•Credit Card Billing Dispute

•Account Closure Procedures

•Claim Procedures

•Premium Payments

•Policy Comparison

•Policy Cancellation or Lapse

•Insurance Renewal Options

•Retirement Planning

•Investment Risk Assessment Questionnaires

•Tax-efficient Investment Strategies

•Investment Performance Enquiry, and many more

•Outbound Calls:

•Credit Card Offers

•Loan Offers

•Loyalty Program Benefits

•Customer Satisfaction Surveys

•EMI Reminder Call

•Policy Upgrade Offers

•Claim Status Updates

•Policyholder Loyalty Benefits

•Insurance Policyholder Surveys

•Term Life Insurance Offer

•Investment Opportunities

•Retirement Savings Review, 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 BFSI 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 BFSI 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 BFSI sector.

•

Smart Assistants and Chatbots: Developing conversational agents and virtual assistants for customer service in the BFSI 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 BFSI 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 BFSI domain call center audio dataset is created by FutureBeeAI and is available for commercial use.

Use Cases

Call Center Conversational AI

Use of speech data for Automatic Speech Recognition

ASR

Chatbot

Language Modelling

TTS

Speech Analytics

Dataset Sample(s)

00:00

ATTRIBUTES

TRANSCRIPTION

LABEL	START	END	CHANNEL	TRANSCRIPT

Speech	0.324	1.149	Speaker 1	Hello Futurebee.
Speech	2.823	3.774	Speaker 2	Hello Futurebee.
Speech	6.524	10.448	Speaker 1	Hi, my name is <PII>John Adams</PII> and you have reached Futurebee financing. How are you?
Speech	12.874	14.285	Speaker 2	Hi, I am good. My name is
Speech	14.711	16.574	Speaker 2	<PII>Cour~ Courtney.</PII>
Speech	17.699	22.199	Speaker 1	<PII>Courtney</PII> Okay, it is good to meet you, <PII>Courtney</PII>. #Ah how can we help you today?
Speech	24.849	33.917	Speaker 2	Well, I just inherited a very large sum of money from my aunt who has died.
Speech	34.517	35.773	Speaker 2	She was a very good person.
Speech	36.148	36.989	Speaker 2	But anyway
Speech	37.589	46.680	Speaker 2	#Ah I just inherited the money from her and so I am looking for a financial advisor because I have never had this much money in my bank account
Speech	47.330	49.121	Speaker 2	and I do not really know what to do with it.
Speech	50.722	63.149	Speaker 1	I will tell what, you are already a step ahead of most people win or inherit money, most of the time the first thing they want to do is spend it on something big and buy gifts for people they like.
Speech	63.597	70.171	Speaker 1	#Ah but more often than not there are fees they do not realize that come with that sort of money.
Speech	70.322	74.224	Speaker 1	#Ah they have told me after few questions find out #Ah what kind of
Speech	74.899	84.233	Speaker 1	what kind of inheritance it was, #Ah what form it maintained. They might give you some ideas how you (()).
Speech	87.596	89.022	Speaker 2	Okay sounds good. I am ready.
Speech	88.838	95.132	Speaker 1	Okay. So the first thing I want to ask is, is this (()) in the form of cash or is this an item?
Speech	97.971	105.748	Speaker 2	#Ah it is, I guess it would be considered cash like I think like it includes some bond as well. I do not really know that counts as cash.
Speech	107.173	114.072	Speaker 1	Okay. A lot of, a lot of people have (()) understanding bond, not a common thing people already know about but that is fine, fine.
Speech	114.947	118.873	Speaker 1	Good to know #Ah and then was it
Speech	119.498	126.947	Speaker 1	gifted to you whether any clauses on the #Ah on the bill perhaps (()) you needed to send that?
Speech	129.323	137.765	Speaker 2	#Ah it did say that I could have spend it all like at one time like I am only allowed to spend so much
Speech	138.215	139.573	Speaker 2	per year.
Speech	140.923	150.145	Speaker 2	#Ah until it you know possibly runs out but I guess that is what I am avoiding is I want to make sure like, like make the money, make money. Right?
Speech	151.546	153.885	Speaker 1	Yeah. Yeah that is, that is very wise. That makes sense.
Speech	154.235	161.019	Speaker 1	We, we can help you do all sorts of things with that. #Ah we will give you certain suggestions like how you can pay off (()) debts,
Speech	161.872	167.822	Speaker 1	#Ah how much of that is going to belong to the government unfortunately #Ah
Speech	168.322	168.572	Speaker 2	Hmm
Speech	168.472	180.044	Speaker 1	how you can invest in property, invest in stock shares, #Ah and your pensions. You can even invest in physical assets, send the money for charity #Ah tax right (()).

TIME

TRANSCRIPT

0.324 - 1.149

Hello Futurebee.

2.823 - 3.774

Hello Futurebee.

6.524 - 10.448

Hi, my name is <PII>John Adams</PII> and you have reached Futurebee financing. How are you?

12.874 - 14.285

Hi, I am good. My name is

14.711 - 16.574

<PII>Cour~ Courtney.</PII>

17.699 - 22.199

<PII>Courtney</PII> Okay, it is good to meet you, <PII>Courtney</PII>. #Ah how can we help you today?

24.849 - 33.917

Well, I just inherited a very large sum of money from my aunt who has died.

34.517 - 35.773

She was a very good person.

36.148 - 36.989

But anyway

37.589 - 46.680

#Ah I just inherited the money from her and so I am looking for a financial advisor because I have never had this much money in my bank account

47.330 - 49.121

and I do not really know what to do with it.

50.722 - 63.149

I will tell what, you are already a step ahead of most people win or inherit money, most of the time the first thing they want to do is spend it on something big and buy gifts for people they like.

63.597 - 70.171

#Ah but more often than not there are fees they do not realize that come with that sort of money.

70.322 - 74.224

#Ah they have told me after few questions find out #Ah what kind of

74.899 - 84.233

what kind of inheritance it was, #Ah what form it maintained. They might give you some ideas how you (()).

87.596 - 89.022

Okay sounds good. I am ready.

88.838 - 95.132

Okay. So the first thing I want to ask is, is this (()) in the form of cash or is this an item?

97.971 - 105.748

#Ah it is, I guess it would be considered cash like I think like it includes some bond as well. I do not really know that counts as cash.

107.173 - 114.072

Okay. A lot of, a lot of people have (()) understanding bond, not a common thing people already know about but that is fine, fine.

114.947 - 118.873

Good to know #Ah and then was it

119.498 - 126.947

gifted to you whether any clauses on the #Ah on the bill perhaps (()) you needed to send that?

129.323 - 137.765

#Ah it did say that I could have spend it all like at one time like I am only allowed to spend so much

138.215 - 139.573

per year.

140.923 - 150.145

#Ah until it you know possibly runs out but I guess that is what I am avoiding is I want to make sure like, like make the money, make money. Right?

151.546 - 153.885

Yeah. Yeah that is, that is very wise. That makes sense.

154.235 - 161.019

We, we can help you do all sorts of things with that. #Ah we will give you certain suggestions like how you can pay off (()) debts,

161.872 - 167.822

#Ah how much of that is going to belong to the government unfortunately #Ah

168.322 - 168.572

Hmm

168.472 - 180.044

how you can invest in property, invest in stock shares, #Ah and your pensions. You can even invest in physical assets, send the money for charity #Ah tax right (()).

Dataset Details

Language

English

Language code

en-us

Country

USA

Accents

Arizona, California ...moreColorado, Connecticut, Delaware, Florida, Georgia, Hawaii, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Alabama, Alaska

Gender Distribution

M:60, F:40

Age Group

18-70