English (US) Call Center Speech Dataset for Travel

The audio dataset comprises call center conversations for the Travel 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 Travel domain designed to enhance the development of call center speech recognition models specifically for the Travel 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 Travel 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:

•Booking inquiries and assistance

•Destination information and recommendations

•Assistance with flight delays or cancellations

•Special assistance for passengers with disabilities

•Travel-related health and safety inquiry

•Assistance with lost or delayed baggage, and many more

•Outbound Calls:

•Promotional offers and package deals

•Customer satisfaction surveys

•Booking confirmations and updates

•Flight schedule changes and notifications

•Customer feedback collection

•Reminders for passport or visa expiration date, 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 Travel 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 Travel 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 Travel sector.

•

Smart Assistants and Chatbots: Developing conversational agents and virtual assistants for customer service in the Travel 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 Travel 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 Travel 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

Noise	0.066	0.313	-	-
Speech	2.192	3.192	Speaker 1	Hello Futurebee.
Speech	4.137	5.227	Speaker 2	Hello Futurebee.
Speech	7.891	11.480	Speaker 1	Hi I am <PII>Michael</PII> With, Futurebee Travel. Whom I speaking with today?
Speech	12.775	14.214	Speaker 2	Hi this is <PII>Mercedes</PII>.
Speech	15.282	16.108	Speaker 1	Hi <PII>Mercedes</PII>.
Speech	16.510	18.318	Speaker 1	[filler] how can I help you today?
Speech	19.673	31.902	Speaker 2	[filler] I just wanted to reach out to your company because I was planning on going on a little vacation. [filler] I was thinking of going to Thailand but honestly I just don't know how
Speech	32.831	36.819	Speaker 2	will the time or even the know how to book it myself.
Speech	37.539	44.517	Speaker 2	So I wanted to find out some of your guys pricing and the services that you offer for [filler] as a travel agency.
Noise	45.505	45.673	-	-
Speech	47.051	57.240	Speaker 1	Okay well you have called the right place [filler] and we would be ecstatic to help you. [filler] real quick. Do you know where in Chiang Mai you want to [filler] do you, do you know where in
Speech	57.981	59.234	Speaker 1	Thailand you want to visit?
Speech	61.317	66.998	Speaker 2	[filler] well I was thinking about maybe one of the like coastal towns but
Noise	67.034	67.287	-	-
Speech	67.709	70.328	Speaker 2	I heard that the mountains are really
Speech	70.757	73.040	Speaker 2	under rated and thought that that might be
Speech	73.745	81.197	Speaker 2	might be a bit different thing than my usual like beach get away to, you know, I go to Mexico sometimes so I thought maybe switch it up a bit.
Speech	82.191	83.492	Speaker 2	So somehwere with the mountains.
Speech	84.239	84.769	Speaker 1	Okay.
Speech	85.364	94.034	Speaker 1	Yeah trying to, trying to branch out. [filler] the reason I actually I said Chiang Mai, as soon as you said Thailand, I thought of Chiang Mai. Chiang Mai is one of my favorite places
Speech	94.504	103.450	Speaker 1	in all of Thailand. Honestly in all of the world. [filler] so, if you are looking for the mountains, Thailand might be a really, Chiang Mai might be a really good place to go.
Speech	104.099	106.498	Speaker 1	[filler] do you want me to look up some information on that?
Speech	108.421	109.763	Speaker 2	Yeah sure go ahead. Very fine.
Noise	110.131	110.787	-	-
Speech	111.558	121.625	Speaker 1	Okay. So while I am getting a few options for you, [filler] could you help me out just, what is your [filler] what is your address? I want to see what airports you close to.
Noise	123.269	123.673	-	-
Speech	123.703	128.593	Speaker 2	[filler] well I am living in San Antonio, Texas right now [filler].
Speech	128.967	133.563	Speaker 2	So I would probably say the San Antonio airport could be the closest.
Speech	133.895	136.752	Speaker 2	But I often use the Austin
Speech	137.131	138.377	Speaker 2	airport as well
Speech	139.063	139.830	Speaker 2	[filler]
Speech	140.895	142.300	Speaker 2	and if need be
Speech	142.770	147.179	Speaker 2	I could even travel to [filler] [noise] to Houston or to Dallas
Noise	144.093	144.342	-	-
Speech	147.877	152.197	Speaker 2	if that really like made a, made a big impact on the price of flights or something like that.
Speech	150.545	150.973	Speaker 1	Okay
Speech	154.324	160.015	Speaker 1	Its very possible it could. So I am glad you are able to tell me that you [filler] have that as an availability.[filler]
Speech	160.497	163.149	Speaker 1	Do you have a time of year you would like to travel?
Speech	164.407	169.111	Speaker 2	Well I just, we just entered the new year right? So my [filler]
Noise	169.460	169.604	-	-
Speech	170.087	173.533	Speaker 2	<initial>PTL</initial> for, for my job just renewed.
Speech	174.306	176.877	Speaker 2	[filler] I don't really want to use it
Speech	178.117	180.425	Speaker 2	[noise] I don't really want to use it all at the beginning.

TIME

TRANSCRIPT

0.066 - 0.313

2.192 - 3.192

Hello Futurebee.

4.137 - 5.227

Hello Futurebee.

7.891 - 11.480

Hi I am <PII>Michael</PII> With, Futurebee Travel. Whom I speaking with today?

12.775 - 14.214

Hi this is <PII>Mercedes</PII>.

15.282 - 16.108

Hi <PII>Mercedes</PII>.

16.510 - 18.318

[filler] how can I help you today?

19.673 - 31.902

[filler] I just wanted to reach out to your company because I was planning on going on a little vacation. [filler] I was thinking of going to Thailand but honestly I just don't know how

32.831 - 36.819

will the time or even the know how to book it myself.

37.539 - 44.517

So I wanted to find out some of your guys pricing and the services that you offer for [filler] as a travel agency.

45.505 - 45.673

47.051 - 57.240

Okay well you have called the right place [filler] and we would be ecstatic to help you. [filler] real quick. Do you know where in Chiang Mai you want to [filler] do you, do you know where in

57.981 - 59.234

Thailand you want to visit?

61.317 - 66.998

[filler] well I was thinking about maybe one of the like coastal towns but

67.034 - 67.287

67.709 - 70.328

I heard that the mountains are really

70.757 - 73.040

under rated and thought that that might be

73.745 - 81.197

might be a bit different thing than my usual like beach get away to, you know, I go to Mexico sometimes so I thought maybe switch it up a bit.

82.191 - 83.492

So somehwere with the mountains.

84.239 - 84.769

Okay.

85.364 - 94.034

Yeah trying to, trying to branch out. [filler] the reason I actually I said Chiang Mai, as soon as you said Thailand, I thought of Chiang Mai. Chiang Mai is one of my favorite places

94.504 - 103.450

in all of Thailand. Honestly in all of the world. [filler] so, if you are looking for the mountains, Thailand might be a really, Chiang Mai might be a really good place to go.

104.099 - 106.498

[filler] do you want me to look up some information on that?

108.421 - 109.763

Yeah sure go ahead. Very fine.

110.131 - 110.787

111.558 - 121.625

Okay. So while I am getting a few options for you, [filler] could you help me out just, what is your [filler] what is your address? I want to see what airports you close to.

123.269 - 123.673

123.703 - 128.593

[filler] well I am living in San Antonio, Texas right now [filler].

128.967 - 133.563

So I would probably say the San Antonio airport could be the closest.

133.895 - 136.752

But I often use the Austin

137.131 - 138.377

airport as well

139.063 - 139.830

[filler]

140.895 - 142.300

and if need be

142.770 - 147.179

I could even travel to [filler] [noise] to Houston or to Dallas

144.093 - 144.342

147.877 - 152.197

if that really like made a, made a big impact on the price of flights or something like that.

150.545 - 150.973

Okay

154.324 - 160.015

Its very possible it could. So I am glad you are able to tell me that you [filler] have that as an availability.[filler]

160.497 - 163.149

Do you have a time of year you would like to travel?

164.407 - 169.111

Well I just, we just entered the new year right? So my [filler]

169.460 - 169.604

170.087 - 173.533

<initial>PTL</initial> for, for my job just renewed.

174.306 - 176.877

[filler] I don't really want to use it

178.117 - 180.425

[noise] I don't really want to use it all at the beginning.

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