The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Enhancing Spoken Text With Punctuation Prediction Using N-Gram Language Model in Intelligent Technical Text Processing Software
|
|
Author(s): Shweta Rani (Jaypee Institute of Information Technology, India)and Rhea Jain (Jaypee Institute of Information Technology, India)
Copyright: 2024
Pages: 17
Source title:
Advancing Software Engineering Through AI, Federated Learning, and Large Language Models
Source Author(s)/Editor(s): Avinash Kumar Sharma (Sharda University, India), Nitin Chanderwal (University of Cincinnati, USA), Amarjeet Prajapati (Jaypee Institute of Information Technology, India), Pancham Singh (Ajay Kumar Garg Engineering College, Ghaziabad, India)and Mrignainy Kansal (Netaji Subhas University of Technology (NSUT), Delhi, India)
DOI: 10.4018/979-8-3693-3502-4.ch013
Purchase
|
Abstract
Communication is a very important practice between two individuals, and for effective communication, the spoken text must be understood by others. Punctuation prediction is utmost essential in spoken text for bridging the language gaps. Various techniques have been proposed in the literature and are also explored. In this work, the authors developed software by studying n- gram model with probability to restore the punctuation in spoken text of technical lectures. In this chapter, the authors compared unigram, bigram, trigram, and quadgram method on varying size of datasets. Findings suggest that trigram model outperform the other for all three datasets and it was also noticed that increasing the gram size more do not have much impact on the performance of the software.
Related Content
|
Frederic Andres.
© 2027.
14 pages.
|
|
Kalsoom Safdar, Khairul Najmy Abdul Rani, Mohd Aminudin Jamlos, Siti Julia Rosli, Muhammad Usman Younus, Zanab Safdar.
© 2027.
27 pages.
|
|
Bani Adam, Binastya Anggara Sekti, Muhammad Adi Zacky Zahran.
© 2027.
24 pages.
|
|
Swetha Margaret T. A., Renuka Devi D..
© 2027.
31 pages.
|
|
Maurice Saluschke, Michael Schulz.
© 2027.
30 pages.
|
|
Mirjam Sepesy Maučec, Gregor Donaj.
© 2027.
16 pages.
|
|
Jorge A. Ruiz-Vanoye, Ocotlan Diaz-Parra, Ricardo A. Barrera-Cámara, Alejandro Fuentes-Penna, Francisco R. Trejo-Macotela, Jaime Aguilar-Ortiz, Eric Simancas-Acevedo.
© 2027.
21 pages.
|
|
|