Book description
Intelligent Speech Signal Processing investigates the utilization of speech analytics across several systems and real-world activities, including sharing data analytics, creating collaboration networks between several participants, and implementing video-conferencing in different application areas. Chapters focus on the latest applications of speech data analysis and management tools across different recording systems. The book emphasizes the multidisciplinary nature of the field, presenting different applications and challenges with extensive studies on the design, development and management of intelligent systems, neural networks and related machine learning techniques for speech signal processing.
- Highlights different data analytics techniques in speech signal processing, including machine learning and data mining
- Illustrates different applications and challenges across the design, implementation and management of intelligent systems and neural networks techniques for speech signal processing
- Includes coverage of biomodal speech recognition, voice activity detection, spoken language and speech disorder identification, automatic speech to speech summarization, and convolutional neural networks
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- About the Editor
- Preface
- Chapter 1: Speech Processing in Healthcare: Can We Integrate?
- Chapter 2: End-to-End Acoustic Modeling Using Convolutional Neural Networks
-
Chapter 3: A Real-Time DSP-Based System for Voice Activity Detection and Background Noise Reduction
- Abstract
- 3.1 Introduction
- 3.2 Microchip dsPIC33 Digital Signal Controller
- 3.3 High Pass Filter
- 3.4 Fast Fourier Transform
- 3.5 Channel Energy Computation
- 3.6 Channel SNR Computation
- 3.7 VAD Decision
- 3.8 VAD Hangover
- 3.9 Computation of Scaling Factor
- 3.10 Scaling of Frequency Channels
- 3.11 Inverse Fourier Transform
- 3.12 Application Programming Interface
- 3.13 Resource Requirements
- 3.14 Microchip PIC Programmer
- 3.15 Audio Components
- 3.16 VAD and Background Noise Reduction Techniques
- 3.17 Results and Discussion
- 3.18 Conclusion and Discussion
- Chapter 4: Disambiguating Conflicting Classification Results in AVSR
-
Chapter 5: A Deep Dive Into Deep Learning Techniques for Solving Spoken Language Identification Problems
- Abstract
- 5.1 Introduction
- 5.2 Spoken Language Identification
- 5.3 Cues for Spoken Language Identification
- 5.4 Stages in Spoken Language Identification
- 5.5 Deep Learning
- 5.6 Artificial and Deep Neural Network
- 5.7 Comparison of Spoken LID System Implementations with Deep Learning Techniques
- 5.8 Discussion
- 5.9 Conclusion
- Chapter 6: Voice Activity Detection-Based Home Automation System for People With Special Needs
-
Chapter 7: Speech Summarization for Tamil Language
- Abstract
- 7.1 Introduction
- 7.2 Extractive Summarization
- 7.3 Abstractive Summarization
- 7.4 Need for Speech Summarization
- 7.5 Issues in the Summarization of a Spoken Document
- 7.6 Tamil Language
- 7.7 System Design for Summarization of Speech Data in Tamil Language
- 7.8 Evaluation Metrics
- 7.9 Speech Corpora for Tamil Language
- 7.10 Conclusion
- Chapter 8: Classifying Recurrent Dynamics on Emotional Speech Signals
- Chapter 9: Intelligent Speech Processing in the Time-Frequency Domain
- Chapter 10: A Framework for Artificially Intelligent Customized Voice Response System Design using Speech Synthesis Markup Language
- Index
Product information
- Title: Intelligent Speech Signal Processing
- Author(s):
- Release date: March 2019
- Publisher(s): Academic Press
- ISBN: 9780128181317
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