UGC Approved Journal no 63975(19)
New UGC Peer-Reviewed Rules

ISSN: 2349-5162 | ESTD Year : 2014
Volume 13 | Issue 2 | February 2026

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Published in:

Volume 12 Issue 7
July-2025
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

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Published Paper ID:
JETIRGX06017


Registration ID:
566932

Page Number

91-99

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Title

SPEECH-TO-TEXT SYSTEMS: A COMPREHENSIVE SURVEY OF TECHNOLOGIES, APPLICATIONS, AND INNOVATIONS

Abstract

Speech-to-Text (STT) technology has become a pivotal tool in human-computer interaction, enabling seamless conversion of spoken language into written text. This paper presents a comprehensive literature survey on STT, exploring its methodologies, applications, and advancements. Traditional STT systems relied on Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs), whereas modern approaches incorporate deep learning models such as Long Short- Term Memory (LSTM) and Transformer-based architectures to enhance accuracy and adaptability. The integration of STT with Natural Language Processing (NLP) techniques, including Named Entity Recognition (NER), sentiment analysis, and summarization, has further refined transcription quality. Applications of STT span across various domains, including accessibility for the visually and hearing impaired, language learning, smart home automation, human-robot interactions, and automated note-taking. Challenges such as handling diverse accents, background noise, and low-resource languages remain key areas for improvement. This survey highlights recent advancements in STT, its integration with IoT, and future research directions to improve efficiency, accuracy, and usability. The findings emphasize the transformative impact of STT technology in enhancing communication, accessibility, and automation across multiple sectors.

Key Words

Speech-to-Text (STT), Automatic Speech Recognition (ASR), Natural Language Processing (NLP), Hidden Markov Models (HMM), Long Short-Term Memory (LSTM), Transformer Models, Smart Home Automation, Human-Robot Interaction, Accessibility, Language Learning, Deep Learning, IoT Integration, Sentiment Analysis, Named Entity Recognition (NER).

Cite This Article

"SPEECH-TO-TEXT SYSTEMS: A COMPREHENSIVE SURVEY OF TECHNOLOGIES, APPLICATIONS, AND INNOVATIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.91-99, July-2025, Available :http://www.jetir.org/papers/JETIRGX06017.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"SPEECH-TO-TEXT SYSTEMS: A COMPREHENSIVE SURVEY OF TECHNOLOGIES, APPLICATIONS, AND INNOVATIONS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp91-99, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06017.pdf

Publication Details

Published Paper ID: JETIRGX06017
Registration ID: 566932
Published In: Volume 12 | Issue 7 | Year July-2025
DOI (Digital Object Identifier):
Page No: 91-99
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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