English (India) Call Center Speech Dataset for Delivery & Logistics

The audio dataset includes call center conversations in Delivery & Logistics, featuring native English speakers from India, with detailed metadata and accurate transcriptions.

Category

Unscripted Call Center Conversations

Total Volume

30 Speech Hours

Last updated

July 2023

Number of participants

60

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About this Off-the-shelf Speech Dataset

About Gradiet Line

What’s Included

Welcome to the English Language Call Center Speech Dataset for the Delivery and Logistics domain. It is a specialized and comprehensive collection of voice data designed to enhance the development of call center speech recognition models specifically for the Delivery and Logistics industry.


With high-quality call center audio recordings, detailed metadata, and accurate transcriptions, it empowers researchers and developers to enhance natural language processing, conversational AI, and generative voice AI algorithms in the Delivery and Logistics domain. Moreover, it facilitates the creation of sophisticated voice assistants and voice bots tailored to the unique linguistic nuances found in the English language spoken in India.


Speech Data:

This training dataset comprises 30 hours of call center audio recordings covering various topics and scenarios related to the Delivery and Logistics domain, to build robust and accurate customer service speech technology.


To curate realistic call center interactions, we collaborated with a diverse network of 60 expert native English speakers from different states/provinces of India. This collaborative effort ensures a balanced representation of Indian accents, dialects, and demographics, promoting inclusivity and reducing biases in the dataset.


Each audio recording captures the essence of unscripted and spontaneous conversations between call center agents and customers, with an average duration ranging from 5 to 15 minutes per call. The dataset includes both inbound and outbound calls, covering scenarios such as inquiries, promotional offers, complaints, technical support, and more. Additionally, the dataset contains call center conversations with both positive and negative outcomes, providing a diverse and realistic dataset.


The speech data is available in WAV format with stereo channels, a bit depth of 16 bits, and a sample rate of 8 kHz, ensuring high-quality audio for accurate analysis. The recording environment is generally quiet, without background noise and echo.


Metadata:

In addition to the audio recordings, our dataset provides comprehensive metadata for each participant. This includes the participant’s age, gender, country, state, and dialect. Additionally, it includes metadata like domain, topic, call type, outcome, bit depth, and sample rate for each conversation.


The metadata serves as a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of English language call center speech recognition models for the Delivery and Logistics domain.


Transcription:

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. The transcriptions capture speaker-wise transcription with time-coded segmentation along with non-speech labels and tags, covering both the agent and customer conversations.


These ready-to-use transcriptions accelerate the development of Delivery and Logistics call center conversational AI and ASR models for the English language.


Updates and Customization:

We understand the importance of collecting data in various environments to build robust ASR models. Therefore, our call center voice dataset is regularly updated with new audio data captured in diverse real-world conditions.


If you require a custom training dataset with specific environmental conditions, we can accommodate your request. We can provide voice data with customized sample rates ranging from 8kHz to 48kHz, allowing you to fine-tune your models for different audio recording setups. Additionally, we can also customize the transcription following your specific guidelines and requirements, to further support your ASR development process.


License:

This Delivery and Logistics call center audio dataset is created by FutureBeeAI and is available for commercial use!


Conclusion:

Whether you are training or fine-tuning speech recognition models, advancing NLP algorithms, or building state-of-the-art voice assistants to improve customer experiences in the Delivery and Logistics sector, our dataset serves as a trusted resource to meet your goals


Use Cases

Use of speech data for Automatic Speech Recognition

ASR

Use of speech data in Conversational AI

Conversational AI

Use of speech data for Chatbot & voicebot creation

Chatbot

Use of speech data in Language Modeling

Language Modelling

Use of speech data in Text-into-speech

TTS

Speech data usecase in Speech Analytics

Speech Analytics

Dataset Sample(s)

Sample Line

ATTRIBUTES

Channel 1Channel 2Format
Female(29)Female(28)wav, json

TRANSCRIPTION

LABELSTARTENDCHANNELTRANSCRIPT
Noise0.8991.548--
Speech1.7746.349Speaker 2Am I talking to customer representative of <initial>ABC</initial> logistics?
Noise6.3748.674--
Speech8.74810.899Speaker 1Thank you for calling <initial>ABC</initial> logistic.
Speech11.39914.599Speaker 1My name is <PII>Tina</PII>. How can I assist you today?
Noise15.77416.297--
Speech18.77423.449Speaker 2Hi <PII>Tina</PII>. I have an issue with my recent delivery.
Speech26.89930.399Speaker 1[noise] What is the issue? [noise]
Noise27.99828.297--
Speech30.79836.899Speaker 2I rece~ I received my package but it [noise] was completely damaged during transit.
Noise37.64937.923--
Speech39.64948.048Speaker 1[noise] I am sorry to hear that. Can you provide me with your order number so I can look into this issue for you?
Noise49.62451.173--
Speech52.34755.923Speaker 2[filler] [noise] Yes, I'll provide it. [noise] Just a minute [noise].
Noise53.44857.298--
Speech58.32359.249Speaker 2Hold on [filler]
Speech59.12460.673Speaker 1Yes sure.
Speech65.44766.899Speaker 2It is <PII>two three</PII>
Speech67.52468.349Speaker 2<PII>six five</PII>
Noise68.19768.748--
Speech69.29969.974Speaker 2<PII>O five</PII>
Speech72.02473.524Speaker 1[filler]Can you repeat it?
Noise74.22474.498--
Speech76.74880.322Speaker 2[filler]Is it the right one <lang:Foreign>ना</lang:Foreign> where the order number should be? Actually?
Noise81.84982.149--
Speech82.32287.322Speaker 1[filler]Do you have the invoice [filler] which you have received with the parcel?
Noise88.42488.649--
Speech88.89993.849Speaker 2Yah Ya. I have it. Just tell me which number will be the invoice number, the order number?
Noise94.74894.998--
Speech95.07298.072Speaker 1[filler]The number at the left hand corner side.
Noise98.17498.498--
Speech101.474108.923Speaker 2Oh. So sorry. I give only another number. Now I'll check it again and I'll give you. Hold a minute [filler].
Speech110.173111.173Speaker 1Okay fine.
Noise111.221111.397--
Speech113.947114.697Speaker 2Yah.
Speech115.373116.721Speaker 2Is this the order number?
Speech117.248118.548Speaker 2[filler]<PII>four five six</PII>
Speech119.548120.572Speaker 2three two one.
Noise124.096124.373--
Speech124.846128.145Speaker 1[filler]Yes yes. Wait a minute. I'll check the details.
Noise125.673126.048--
Noise128.348128.823--
Speech129.798130.747Speaker 2Okay.
Noise138.020138.473--
Speech139.372141.270Speaker 1[filler]Thank you.
Speech141.895146.270Speaker 1I can see that your package was delivered yesterday. Am I right?
Noise146.395146.872--
Speech147.872150.895Speaker 2Yah yah yesterday evening I got the package.
Noise152.622153.747--
Speech153.848164.020Speaker 1Okay. [filler] I apologies for the inconvenient caused by the packa~ damage to the package. [filler] Can you provide me with more details about the damage?
Noise164.173164.448--
Noise164.923165.473--
Speech166.848172.673Speaker 2Yah I'll help you out. Just let me know what the things you want to know? What details you want?
Noise173.372173.622--
Speech173.973187.173Speaker 1[filler]I was [filler] how the package has been [filler] damage actually? Is the product inside a package is [filler] okay or only the product is also damage or something like that?

TRANSCRIPTION

TIMETRANSCRIPT
0.899
1.548
-
1.774
6.349
Am I talking to customer representative of <initial>ABC</initial> logistics?
6.374
8.674
-
8.748
10.899
Thank you for calling <initial>ABC</initial> logistic.
11.399
14.599
My name is <PII>Tina</PII>. How can I assist you today?
15.774
16.297
-
18.774
23.449
Hi <PII>Tina</PII>. I have an issue with my recent delivery.
26.899
30.399
[noise] What is the issue? [noise]
27.998
28.297
-
30.798
36.899
I rece~ I received my package but it [noise] was completely damaged during transit.
37.649
37.923
-
39.649
48.048
[noise] I am sorry to hear that. Can you provide me with your order number so I can look into this issue for you?
49.624
51.173
-
52.347
55.923
[filler] [noise] Yes, I'll provide it. [noise] Just a minute [noise].
53.448
57.298
-
58.323
59.249
Hold on [filler]
59.124
60.673
Yes sure.
65.447
66.899
It is <PII>two three</PII>
67.524
68.349
<PII>six five</PII>
68.197
68.748
-
69.299
69.974
<PII>O five</PII>
72.024
73.524
[filler]Can you repeat it?
74.224
74.498
-
76.748
80.322
[filler]Is it the right one <lang:Foreign>ना</lang:Foreign> where the order number should be? Actually?
81.849
82.149
-
82.322
87.322
[filler]Do you have the invoice [filler] which you have received with the parcel?
88.424
88.649
-
88.899
93.849
Yah Ya. I have it. Just tell me which number will be the invoice number, the order number?
94.748
94.998
-
95.072
98.072
[filler]The number at the left hand corner side.
98.174
98.498
-
101.474
108.923
Oh. So sorry. I give only another number. Now I'll check it again and I'll give you. Hold a minute [filler].
110.173
111.173
Okay fine.
111.221
111.397
-
113.947
114.697
Yah.
115.373
116.721
Is this the order number?
117.248
118.548
[filler]<PII>four five six</PII>
119.548
120.572
three two one.
124.096
124.373
-
124.846
128.145
[filler]Yes yes. Wait a minute. I'll check the details.
125.673
126.048
-
128.348
128.823
-
129.798
130.747
Okay.
138.020
138.473
-
139.372
141.270
[filler]Thank you.
141.895
146.270
I can see that your package was delivered yesterday. Am I right?
146.395
146.872
-
147.872
150.895
Yah yah yesterday evening I got the package.
152.622
153.747
-
153.848
164.020
Okay. [filler] I apologies for the inconvenient caused by the packa~ damage to the package. [filler] Can you provide me with more details about the damage?
164.173
164.448
-
164.923
165.473
-
166.848
172.673
Yah I'll help you out. Just let me know what the things you want to know? What details you want?
173.372
173.622
-
173.973
187.173
[filler]I was [filler] how the package has been [filler] damage actually? Is the product inside a package is [filler] okay or only the product is also damage or something like that?

Dataset Demographics

Details Headline

Language

English

Language code

en-In

Country

India

Accents

Chandigarh,...more

Gender Distribution

M:55, F:45

Age Group

18-70

Audio File Details

Details Headline

Environment

Silent, Noisy

Bit Depth

16 bit

Format

wav

Sample rate

8khz

Channel

Dual separate channel

Audio file duration

5-15 minutes

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