| Journal of Smart Sensors and Computing
Received: 18 February 2025; Revised: 25 March 2025; Accepted: 15 May 2025; Published Online: 25 May 2025.
J. Smart Sens. Comput., 2025, 1(1), 25203 | Volume 1 Issue 1 (June 2025) | DOI: https://doi.org/10.64189/ssc.25203
© The Author(s) 2025
This article is licensed under Creative Commons Attribution NonCommercial 4.0 International (CC-BY-NC 4.0)
Attendie AI: A Smart and Proactive Solution for
Overlapping Meetings Using Natural Language
Processing and Machine Learning
Jaimeet Sarode,
*
Arya Teli, Nilesh Apsingkar and Miheer Abhyankar
Department of Computer Science and Engineering, MIT ADT University, Pune, Maharashtra, 412201, India
*Email: jaimeetsarode@gmail.com (Jaimeet Sarode)
Abstract
When put on a conference table, the smart device Attendie records sessions and produces a text file that can be
downloaded along with bulleted summaries. It can participate in meetings that are held online using Zoom,
Google Meet, or Microsoft Teams and notify the user when their name is called during the meeting. In addition,
Attendie may automatically join meetings and connect with the user's Google calendar. Attendie recognizes and
transcribes audio signals using computational linguistics to translate spoken words into text. In this procedure,
sophisticated machine learning models select and convert audio inputs into text that can be updated and saved
on a specific device using linguistic algorithms. The words are also converted into Unicode characters during
transcription to make them easier to display and compatible with a variety of hardware and software. The ability
to instantly attend online meetings and a notification function that warns users when their name is mentioned
are just two of Attendie's many advantages over its rivals.
Keywords: Machine learning algorithms; Data analysis techniques; Artificial intelligence applications; Deep learning
frameworks; Computational efficiency in AI models.
1. Introduction
-paced world, especially within corporate environments and academic institutions, efficient
management of meetings has become increasingly critical. Attendie AI is an innovative tool designed to enhance
productivity during meetings by automating tasks such as transcription, summarization, and notifications. This
tool aims to assist users by "attending" meetings on their behalf when they have overlapping schedules or
cannot join due to other commitments.
[1]
By recording, transcribing, and summarizing the content of
discussions, Attendie ensures that users remain informed on essential points and follow-up actions, minimizing
the risk of missing key information.
[2]
Effective meeting management is essential, particularly in collaborative workplaces where missed information
or incomplete notes can result in delays for subsequent tasks.
[3]
In busy office settings, employees often struggle
to keep up with multiple simultaneous meetings, which can lead to gaps in information and hinder productivity.
Similarly, in universities, students may miss lectures due to conflicting schedules, impacting their learning
experience and academic performance. A system like Attendie can help resolve these issues by enabling users
to stay updated on essential discussions without needing to attend every meeting or lecture physically.
[4]
The primary issue that Attendie AI addresses is straightforward yet significant: it helps users avoid missing
important information from meetings they cannot attend. By providing concise summaries and notifying users
when their names are mentioned or when they are assigned tasks, Attendie ensures that critical details are
never overlooked. Whether applied in corporate offices, educational institutions, or even casual group meetings,
Attendie is designed to save time and improve overall productivity.
[5]
2. Literature review
The increasing need for efficient meeting and lecture management systems has spurred research and
development in technologies like Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and
artificial intelligence-driven summarization. Attendie AI aims to use these technologies to provide an innovative
solution that captures audio, transcribes speech, summarizes content, and integrates with online platforms,
making it an essential tool in corporate and academic settings.
2.1 Automatic Speech Recognition (ASR) technology
Automatic Speech Recognition has become a foundational technology in the development of smart meeting
assistants. ASR enables the conversion of spoken language into text, which can be processed further for
summarization, analysis, and storage. A notable ASR system is Whisper, developed by OpenAI, which is known
for its high accuracy and capability to handle diverse accents, languages, and environments
[6]
is designed to
perform well in challenging acoustic conditions, making it suitable for real-time meeting environments where
audio clarity may vary due to background noise, speaker variability, and other factors.
Studies in ASR technology, such as by Graves et al., emphasize the importance of deep neural networks and
recurrent architectures for accurate transcription. The introduction of Connectionist Temporal Classification
(CTC) has further improved ASR accuracy by enabling networks to process variable-length sequences without
requiring precise alignment between audio inputs and text outputs.
[7]
TransfD ASR models, such as those
discussed by Dong et al., further enhance the capabilities of ASR systems by enabling parallel processing and
better handling of long dependencies within audio data.
[8]
-based deep learning architectures aligns with these advances, making it an ideal
choice for real-time applications. In addition, OpenAI has trained Whisper on a vast dataset that includes diverse
dialects and accents, providing an ASR model that can handle global user bases in corporate and academic
environments. This adaptability is critical for meeting assistants like Attendie AI, as users may come from
various linguistic backgrounds and settings.
2.2 Natural Language Processing (NLP) for summarization
Once the ASR transcribes the spoken language, NLP techniques are used to summarize the content into key
points. Summarization is essential for reducing information overload, especially in long meetings where not all
details are relevant to every participant. NLP-based summarization techniques can be broadly categorized into
extractive and abstractive summarization.
Extractive summarization selects important sentences or phrases directly from the transcript, while abstractive
summarization involves generating new sentences to encapsulate the main ideas.
[9]
would benefit from abstractive models, which are more advanced but provide concise and coherent summaries
that enhance readability and retain critical information.
Transformer-based models, especially those based on BERT (Bidirectional Encoder Representations from
Transformers), have revolutionized NLP tasks, including summarization.
[10]
BERT's bidirectional approach all
capture context more effectively than previous models, making it particularly useful for summarizing meeting
content where understanding the relationship between ideas is crucial. Attendie AI may utilize these
transformer models to extract meaningful insights from conversations and distil them into short, bulleted
summaries.
A further advancement in NLP is the T5 (Text-To-Text Transfer Transformer) model by Raffel et al., which treats
every NLP task as a text-generation problem. This approach is particularly beneficial for tasks that require
flexibility in language generation, such as abstractive summarization.
[11]
With transformer-based
summarization model AI can generate summaries that are not only concise but also maintain the semantic
integrity of the original content, providing users with an efficient way to review essential points.
2.3 Integration with online meeting platforms
With the rise of remote work and online education, integration with online meeting platforms like Zoom, Google
Meet, and Microsoft Teams has become critical for AI-driven meeting assistants. These platforms offer APIs that
allow third-party applications to join meetings, capture audio, and interact with participants. Attendie AI
leverages these integrations to ensure that it can participate in meetings, record audio, and notify users of key
mentions and tasks, even in virtual environments.
integration for accessibility and functionality.
[12]
By connecting with online meeting platforms, Attend perform
real-time transcription, send notifications when specific names are mentioned, and even provide summaries
after the meeting concludes. These integrations enable the tool to be versatile, functioning in hybrid
environments and catering to both in-person and virtual participants.
2.4 Machine learning for personalized notifications and task management
Apart from transcription and summarization, Attendie AI differentiates itself by notifying users when their
name is mentioned or when they are assigned tasks. This feature requires machine learning models that can
detect specific keywords, phrases, and user-specific identifiers. Research by Vaswani et al. introduced the
Transformer model, which is adept at understanding sequences and is widely used for keyword extraction and
notification systems.
[13]
These capabilities are crucial for Attendie AI, as they allow to actively monitor
discussions and notify users about relevant information.
Furthermore, the ability to personalize notifications based on user preferences and priorities can be
reduce irrelevant notifications, focusing only on high-priority mentions. Studies on RL in notification systems,
such as those by Li et al., demonstrate the effectiveness of this approach in improving user satisfaction and
minimizing distractions.
[14]
2.5 Challenges and future directions
The development of Attendis forward certain challenges, including data privacy and ethical considerations.
Recording and transcribing meetings involve capturing sensitive information, which raises privacy concerns.
According to Rajpurkar et al., ensuring data security in ASR and NLP systems requires robust anonymization
techniques and compliance with data protection regulations like GDPR.
[15]
Attendie AI must implement secure
data handling practices to protect user forever, advancements in sentiment analysis and emotion detection in
NLP can be explored to enhance Attendie AI. Emotion detection could allow the system to provide additional
insights into the sentiment of the meeting, identifying potential concerns or action points that require
immediate attention. This direction aligns with recent research in affective computing, which examines the
intersection of AI and emotional intelligence.
[16]
3. Prior art
3.1 Otter.ai
Otter.ai offers real-time transcription for meetings, webinars, and interviews. Its standout features include AI-
generated meeting summaries, speaker identification, and the ability to capture slides. The platform integrates
seamlessly with tools like Zoom, Microsoft Teams, Google Meet, and Slack, allowing automated transcription
and note-sharing during meetings. Otter also provides mobile apps and browser extensions for enhanced
accessibility.
The platform supports live collaboration with shared meeting notes and action items, ideal for teams. Updates
can be synced with tools like Slack and HubSpot for productivity. Supports platforms like Salesforce, Google
Workspace, Dropbox, and Amazon S3, making it suitable for business teams and sales operations.
Otter is primarily focused on transcription and meeting-related tasks. It may lack features for industries needing
advanced audio or video editing tools. Ideal for professionals, businesses, and the education sector, particularly
for hybrid or virtual meetings. We have collected this information from their official website.
[17]
3.2 Meeting Owl 4
The Meeting Owl 4+, released in 2024, is Owl Labs' flagship product aimed at hybrid meeting environments. It
builds upon its predecessors with improved hardware, AI-driven features, and seamless integration capabilities,
ensuring enhanced communication and collaboration for hybrid teams.
Features a 64 MP sensor with a 4K Ultra HD resolution for superior video quality. It offers a 360° panoramic
view with auto-focus on active speakers, using the Owl Intelligence System to track visual and audio cues
dynamically. It tracks motion, voice, and facial cues to focus on the active speaker.
Equipped with 8 omnidirectional smart microphones, it ensures clear audio pickup within a 5.5-meter radius.
The system automatically equalizes speaker volume to amplify quiet voices. Dual integrated speakers provide
360° sound coverage with a maximum output of 79 dB SPL, ensuring clear in-room communication. Compatible
with USB-C, Enterprise WIFI, and Ethernet for flexible deployment in different spaces.
Works with most video conferencing platforms such as Zoom, Microsoft Teams, and Webex. For larger spaces,
the Meeting Owl 4+ can connect with additional devices like another Meeting Owl creating a multi-camera
ecosystem for comprehensive coverage. Supports hybrid brainstorming with integrations like the Whiteboard
Owl, which makes in-room content digitally accessible to remote participants, managed via the Meeting Owl
App. Add-ons like the Expansion Mic can extend the microphone range by an additional 2.5 meters. Premium
cost compared to standard video conferencing cameras, Meeting Owl 4+ costs $1,999. We have collected this
information from their official website.
[18]
4. Unique value proposition of Attendie AI
4.1 Ease of use as a standalone device
One of the strongest advantages of Attendie AI is its simplicity and ease of use. Unlike many other meeting
management solutions that may require complex setups or software installations, Attendie AI functions as a
standalone device. It can be easily placed in a meeting room or lecture hall, without requiring additional
hardware or technical expertise. This makes it useful for a wide range of users, from corporate professionals to
university staff, ensuring that anyone can utilize the tool without specialized training.
[19]
4.2 Comprehensive feature set
Attendie AI offers a well-rounded set of features that address all key aspects of meeting management. It
transcribes audio in real time, summarizes lengthy discussions into key points, and integrates with Zoom,
Google Meet, and Microsoft Teams. Users can also receive notifications when their names are mentioned, or
when they are assigned tasks. This combination of transcription, summarization, and personalized notifications
streamlines the entire meeting process, making it easy to capture important information, even after a meeting
is completed.
[20]
4.3 State-of-the-art technology
Attendie AI leverages advanced technologies, including Whisper API for real-time transcription and
Transformer-
correctly even in challenging environments with background noise or diverse accents. The NLP models used for
summarization are trained to condense large amounts of spoken information into concise summaries, helping
users to focus on key points without having to sift through lengthy transcripts. This state-of-the-art technology
ensures that Attendie AI provides accurate and efficient results in both the corporate and academic
environments.
[21]
4.4 Advantages over other tools in the market
Attendie AI distinguishes itself from its competitors by offering a fully automated and comprehensive solution
for meeting management. Other tools may focus solely on transcription or require manual intervention for
summarization; however, Attendie AI handles the entire process autonomously. Its integration with multiple
meeting platforms, combined with its ability to provide notifications and summaries, sets it apart as a more
holistic tool. Additionally, the standalone nature of the device makes it more convenient than software-based
tools that may require installation, updates, or compatibility checks.
[19]
5. Methodology
Attendie AI is designed to simplify the process of managing meetings and lectures using advanced technologies
for transcription, summarization, and integration with online platforms. Attendie AI flowchart is shown in the
Fig. 1.
Fig. 1: Attendie AI flowchart.
The system begins by capturing real-time audio using the Whisper API, an Automatic Speech Recognition (ASR)
tool developed by OpenAI.
[22]
This tool helps convert spoken words into text with a high degree of accuracy,
ensuring that even complex or technical language is captured properly. By working in real time, Whisper allows
Attendie AI to provide instant feedback and transcription during meetings and lectures.
Once the transcription is complete, Attendie AI uses transformer-based Natural Language Processing (NLP)
models to summarize the discussion. These models can understand long and detailed conversations and
condensing the content into easy-to-read key points or bullet summaries.
[23]
This saves time for users who do
not want to go through the entire transcription but still require important highlights.
Finally, Attendie AI seamlessly integrates with popular online meeting platforms such as Zoom, Google Meet,
and Microsoft Teams.
[24]
This makes it useful not only for in-person meetings, but also for remote or hybrid
setups, allowing Attendie to work in multiple environments and cater to a wide range of users, from students
attending virtual lectures to professionals in corporate meetings.
5.1 Block diagram description
Attendie is placed in the center of the meeting rooms and captures the meeting's audio and video. The data is
then sent to the cloud server, where it is processed to generate real-time transcription and intelligent key
highlights. Attendie performs keyword tagging, which involves recognizing the topics spoken in the meeting and
generating related summaries. Furthermore, Attendie includes action item tracking, highlighting the name of
the person whenever he/she is given a certain task or when the person's name is called out. The processed data
includes identification of the speaker, keyword tagging, and action item tracking, organizing the information
efficiently for easy retrieval and understanding. The processed information is sent to the client and backed up
on the server.
5.2 Real-time audio capture
Attendie AI initiates the process by capturing real-time audio from the meeting environment. This critical step
is made possible through integration with the Whisper API, which facilitates seamless and high-fidelity audio
capture.
[22]
Attendie AI is equipped with external audio input devices to ensure flexibility and adaptability to
various meeting setups.
5.3 Whisper API integration
Whisper, an Automatic Speech Recognition (ASR) system developed by OpenAI, is used in Attendie AI's audio
capture capability. This API is powered by state-of-the-art deep learning models that have been trained on
extensive datasets of spoken language, making it highly accurate and versatile.
[22]
Whisper directly processes
raw audio waveforms using a deep learning architecture, typically based on transformer models. This approach
allows it to handle a wide range of audio quality, accents, and languages.
5.4 ASR technology
Whisper's ASR technology excels in accurately transcribing spoken words into text, even in challenging acoustic
environments. It is specifically fine-tuned to handle diverse accents, languages, and speaking styles, ensuring a
high degree of transcription accuracy.
[22]
It is trained on a large corpus of text and spoken data, which allows it
to handle various languages, dialects, and accents. This transformer model is designed to predict sequences of
words based on the context of the audio, ensuring more accurate transcription even in noisy environments or
with various speaking styles
5.5 Real-time processing
Whisper API as shown in the Fig. 2 enables Attendie AI to perform real-time audio processing, which is crucial
for capturing meetings as they happen.
[25]
This real-time capability ensures that meeting content is immediately
available for transcription and subsequent stages of the methodology.
[22]
Whisper uses a greedy search as the
default decoding algorithm for transcribing audio. This means the model selects the most probable
transcription at each step based on the given acoustic and language model predictions. There is an option for
beam search, but it is not the default. In some cases, Whisper can utilize beam search to explore multiple
potential transcriptions, selecting the one with the highest overall likelihood by considering various possible
sequences.
[26]
5.6 Natural Language Processing (NLP) summarization
After obtaining the real-time transcription, Attendie AI applies Natural Language Processing (NLP) techniques
to generate concise and coherent summaries of the meeting's content.
Fig. 2: Whisper API working.
5.7 Transformer-based NLP models for summarization
Attendie AI leverages Transformer-based NLP (Natural Language Processing) models to perform the crucial
task of summarizing meeting content. These models represent a significant advancement in NLP and are pivotal
in Attendie AI's ability to distil lengthy discussions into concise and coherent summaries.
5.8 Model preparation
When using models like T5 for text summarization, you first tokenize the input text using the corresponding
tokenizer (AutoTokenizer). The Hugging face transformer models are shown in the Fig. 3.
[27]
For example, when
using T5, a common practice is to prepend a task-specific prefix (e.g., "summarize:") to the input text. This helps
the model understand the task it needs to perform.
Fig. 3: Hugging face transformer models.
5.9 Adaptation to meeting context
Fine-Tuning to make the summarization process effective in the context of meetings, Attendie AI fine-tunes
these Transformer models. Fine-tuning involves training the models on meeting-specific data, including
transcripts from various types of meetings and discussions. During fine-tuning, the models adapt to meeting-
specific terminology, phrases, and nuances. This ensures that the generated summaries are not only concise but
also highly relevant to the content discussed in meetings.
5.10 Continuous improvement
Attendie AI's use of Transformer models is not static. The system continually learns and adapts based on user
interactions and feedback. This iterative learning process contributes to improving the quality of the summaries
generated over time.
5.11 Multilingual support
Transformer-based models are versatile and can be extended to support multiple languages. Attendie AI can be
fine-tuned to provide meeting summarization in various languages, increasing its accessibility to a global user
base. Transformer Architecture, reproduced with the permission form
[28]
as shown in the Fig. 4.
Fig. 4: Transformer Architecture.
5.12 Software system description
The system requirements for Attendie AI can vary depending on the scale of deployment and the transcription
model being used. Here are the general minimum system requirements for running Attendie AI on medium
transcription model:
1. Minimum system requirements:
• CPU: Hexa-core processor (Example: - Ryzen 5 4600h or equivalent)
• RAM: 8gb or more
• GPU: Nvidia GPU for hardware acceleration. Preferably RTX 3050 or its AMD equivalent.
• Operation System: Windows 10 or higher / Ubuntu server LTS
• Internet connection: High speed internet connection
2. Additional considerations:
• GPU Acceleration: Using a high-end GPU can significantly accelerate certain tasks, such as deep learning-
based transcription and NLP processing.
• Multithreading Support: Ensure that the hardware supports multithreading, as Attendie AI benefits from
multithreading for parallel processing.
6. Conclusion
In conclusion, Attendie AI provides a comprehensive solution to the challenges faced by modern management.
Offering real-time transcription, intelligent summarization, and integration with popular meeting platforms
ensures that no critical information is lost, even in overlapping meetings or absences. Its ease of use, as a
standalone device, coupled with cutting-edge technology such as Whisper API and Transformer-based NLP
models, makes it a valuable tool for both corporate and academic settings. Attendie AI not only boosts
productivity but also improves decision-making by ensuring that all meeting participants stay informed and up-
to-date. Looking ahead, Attendie AI holds the potential to further enhance workplace and educational efficiency,
making it an indispensable asset for individuals and organizations.
Acknowledgment
We would like to express my sincere gratitude to everyone who contributed to the successful completion of this
project. First and foremost, we are deeply grateful to our advisors Dr. Suvarna Pawar and Dr. Santosh Darade for
their invaluable guidance, insights, and support throughout the research process. Their expertise and
encouragement played a crucial role in shaping this project. We would also like to extend our thanks to our
colleagues and friends who provided helpful feedback and encouragement, making the journey both
collaborative and inspiring.
Supporting Information
Not applicable.
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