This book entitled, "Introduction to Speech and Language Therapy", has been designed to add to the knowledge of researchers, scholars and the students of second language learners to enlighten them with various aspects of the speech, the language and the methods and techniques of its acquisition. It takes the readers through an overview of speech and language therapy and how these concepts assist the children and the adults in gaining the intricate nuances of the language. It also talks about the role and technique of speech and language proper and its assessment in the teaching of second language. Additionally, the book sheds light on the linguistic theories of speech, the teaching of language for children and the techniques of vocabulary development.
Automatic Speech Recognition (ASR) is one of the greatest technical challenges of modern times and has been attracting the attention of researchers around the world for more than half a century. As with all speech technologies, this is a multidisciplinary problem that requires knowledge in many areas, from acoustics, phonetics and linguistics, to mathematics, telecommunications, signal processing and programming. A special problem is the fact that it is a problem that is extremely language-dependent.
Speech recognition or speech to text includes capturing and digitizing the sound waves, transformation of basic linguistic units or phonemes, constructing words from phonemes and contextually analyzing the words to ensure the correct spelling of words that sounds the same. Approach: Studying the possibility of designing a software system using one of the techniques of Artificial Intelligence (AI) applications neuron networks where this system is able to distinguish the sound signals and neural networks of irregular users. Fixed weights are trained on those forms first and then the system gives the output match for each of these formats and high speed. The proposed neural network study is based on solutions of speech recognition tasks, detecting signals using angular modulation and detection of modulated techniques.
Author(s): Zoran Gacovski
Publisher: AclerPress
Year: 2023
Language: English
Pages: 428
Cover
Title Page
Copyright
DECLARATION
ABOUT THE EDITOR
TABLE OF CONTENTS
List of Contributors
List of Abbreviations
Preface
Section 1: Methods and Approaches for Speech Recognition
Chapter 1 Artificial Intelligence for Speech Recognition Based on Neural Networks
Abstract
Introduction
Pattern Recognition
Neural Networks
Procedure Works
Conclusion
References
Chapter 2 An HMM-Like Dynamic Time Warping Scheme for Automatic Speech Recognition
Abstract
Introduction
Speech Recognition By DTW
The Proposed Hmm-Like DTW Approach for Speech Recognition
Hmm-Like DTW
Experiments And Results
Conclusions
References
Chapter 3 Direct Recovery of Clean Speech Using a Hybrid Noise Suppression Algorithm for Robust Speech Recognition System
Abstract
Introduction
System Model
Algorithm Description
Algorithm Evaluation
Conclusion
Supplementary Materials
References
Chapter 4 Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition
Abstract
Introduction
Materials and Methods
Results and Discussion
Conclusions
Acknowledgments
References
Chapter 5 A Fast Learning Method for Multilayer Perceptrons in Automatic Speech Recognition Systems
Abstract
Introduction
A Fast Learning Method
Experiments
Conclusions
Acknowledgments
References
Section 2: Speech Recognition for Different Languages
Chapter 6 Development of Application Specific Continuous Speech Recognition System in Hindi
Abstract
Introduction
Automatic Speech Recognition System
The Training Methodology
Evaluation Methodology
Results And Discussion
Conclusion And Future Work
References
Chapter 7 Multitask Learning with Local Attention for Tibetan Speech Recognition
Abstract
Introduction
Related Work
Methods
Experiments
Conclusions
Authors’ Contributions
Acknowledgments
References
Chapter 8 Arabic Speech Recognition System Based on MFCC and HMMs
Introduction
Mel Frequency Cepstral Coefficients (Mfcc)
Hidden Markov Model (Hmm)
Experimental Results
Conclusion
References
Chapter 9 Using Morphological Data in Language Modeling for Serbian Large Vocabulary Speech Recognition
Abstract
Introduction
Relevant Previous Work
Materials And Methods
Results And Discussion
Conclusions
Data Availability
Acknowledgments
References
Chapter 10 Phoneme Sequence Modeling in the Context of Speech Signal Recognition in Language “Baoule”
Abstract
Introduction
The Speech Signals
System Overview
Hidden Markov Model Discrete Time
Implementation
Conclusions
Annexes
References
Section 3: Applications with Speech Recognition
Chapter 11 An Overview of Basics Speech Recognition and Autonomous Approach for Smart Home IOT Low Power Devices
Abstract
Introduction
Overview State of the Art
Our Methodology
Algorithm Description
Recognition Technique
Results
Conclusion
References
Chapter 12 BigEar: Ubiquitous Wireless Low-Budget Speech Capturing Interface
Abstract
Introduction
Related Work
Bigear Architecture
Speech Acquisition Model and Implementation
Speech Reconstruction
Bigear Simulation and Model Validation
Experimental Results and Evaluation
Conclusions and Future Works
References
Chapter 13 Using Speech Recognition in Learning Primary School Mathematics via Explain, Instruct and Facilitate Techniques
Abstract
Introduction
Materials and Methods
Results and Discussions
Conclusions and Recommendations
Acknowledgements
References
Chapter 14 A Prototype of a Semantic Platform with a Speech Recognition System for Visual Impaired People
Abstract
Introduction
Review of Literature
Current Problems of Web Platforms for Accessibility
Prototype of Semantic Platform with Speech Recognition System
Conceptual Scheme of Architecture
Expected Contributions and Future Work
References
Section 4: Language Understanding Technology
Chapter 15 English Sentence Recognition Based on HMM and Clustering
Abstract
Introduction
Whole Design Process
Core Algorithm
Experimental Results and Analysis
Conclusion
Acknowledgements
References
Chapter 16 A Comparative Study to Understanding about Poetics Based on Natural Language Processing
Abstract
Introduction
Materials and Method
Results
Discussion
Conclusion
References
Chapter 17 Semi-Supervised Learning of Statistical Models for Natural Language Understanding
Abstract
Introduction
Related Work
The Proposed Framework
Experimental Results
Conclusions
Acknowledgments
References
Chapter 18 Linguistic Factors Influencing Speech Audiometric Assessment
Abstract
Introduction
Linguistic Cues to Speech Understanding
Aim And Research Questions
Syntactic Complexity, Cognitive Load, and Speech Understanding
The Role of Open Versus Closed Word Classes In Sentence Understanding
Materials and Method
Results
Discussion
Conclusion
Acknowledgments
References
Index
Back Cover
