Table of Contents
Chapter 1: Speech Processing in Healthcare: Can We Integrate?
Chapter 2: End-to-End Acoustic Modeling Using Convolutional Neural Networks
2.3 Various Architecture of ASR
2.4 Convolutional Neural Networks
2.5 CNN-Based End-to-End Approach
2.6 Experiments and Their Results
Chapter 3: A Real-Time DSP-Based System for Voice Activity Detection and Background Noise Reduction
3.2 Microchip dsPIC33 Digital Signal Controller
3.5 Channel Energy Computation
3.9 Computation of Scaling Factor
3.10 Scaling of Frequency Channels
3.11 Inverse Fourier Transform
3.12 Application Programming Interface
3.16 VAD and Background Noise Reduction Techniques
3.18 Conclusion and Discussion
Chapter 4: Disambiguating Conflicting Classification Results in AVSR
4.2 Detection of Conflicting Classes
4.3 Complementary Models for Classification
4.4 Proposed Cascade of Classifiers
5.2 Spoken Language Identification
5.3 Cues for Spoken Language Identification
5.4 Stages in Spoken Language Identification
5.6 Artificial and Deep Neural Network
5.7 Comparison of Spoken LID System Implementations with Deep Learning Techniques
Chapter 6: Voice Activity Detection-Based Home Automation System for People With Special Needs
6.2 Conceptual Design of the System
Chapter 7: Speech Summarization for Tamil Language
7.4 Need for Speech Summarization
7.5 Issues in the Summarization of a Spoken Document
7.7 System Design for Summarization of Speech Data in Tamil Language
7.9 Speech Corpora for Tamil Language
Chapter 8: Classifying Recurrent Dynamics on Emotional Speech Signals
8.2 Data Collection and Processing
8.4 Numerical Experiments and Results
Chapter 9: Intelligent Speech Processing in the Time-Frequency Domain
9.1 Wavelet Packet Decomposition
9.2 Empirical Mode Decomposition
9.3 Variational Mode Decomposition
9.4 Synchrosqueezing Wavelet Transform: EMD Like a Tool
9.5 Applications of the Decomposition Technique
10.4 Amazon Voice Service (AVS)