Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156632
Title: Deep-learning for conversational speech using semantic textual analysis
Authors: Suthakar, Shiny Gladdys
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Computer science and engineering::Computing methodologies::Document and text processing
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Suthakar, S. G. (2022). Deep-learning for conversational speech using semantic textual analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156632
Project: SCSE21-0063
Abstract: Automatic Speech Recognition (ASR) systems today have a prominent and widespread impact among software applications of different domains. They are usually embedded in the applications to provide user input to the main functionality, hence, acting as the cornerstone of these applications, especially potentially life-saving ones. However, most ASR systems today can only work effectively on formal speech input. They have a lot of room to fully understand speech of colloquial nature. Focusing on English speech in the Singaporean context, this project aims to provide a solution for generating formal semantic equivalents of conversational sentences derived from speech. Thus, acoustic and language models of existing ASR systems can be trained with these mappings from conversational to formal text, thus acquiring better comprehension and performance when receiving informal speech input. Furthermore, this project aims to analyse the semantic similarity performance of deep-learning models in terms of semantically similar formal sentence generation and their use of deep-learning techniques. The experimentation results show that the PEGASUS model performs better holistically. This report will present the proposed solution framework and lay out in detail the components of the project implementation.
URI: https://hdl.handle.net/10356/156632
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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