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Intelligent speech signal processing

Author: Nilanjan Dey
Publisher: London : Academic Press, [2019]
Edition/Format:   eBook : Document : English : First editionView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
(OCoLC)1076806164
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Nilanjan Dey
ISBN: 9780128181317 0128181311 9780128181300 0128181303
OCLC Number: 1090800280
Description: 1 online resource : illustrations (some color)
Contents: Front Cover; Intelligent Speech Signal Processing; Copyright; Contents; Contributors; About the Editor; Preface; Chapter 1: Speech Processing in Healthcare: Can We Integrate?; References; Chapter 2: End-to-End Acoustic Modeling Using Convolutional Neural Networks; 2.1. Introduction; 2.2. Related Work; 2.3. Various Architecture of ASR; 2.3.1. GMM/DNN; 2.3.2. Attention Mechanism; 2.3.3. Connectionist Temporal Classification; 2.4. Convolutional Neural Networks; 2.4.1. Type of Pooling; 2.4.1.1. Max Pooling; 2.4.1.2. Average Pooling; 2.4.1.3. Stochastic Pooling; 2.4.1.4. Lp Pooling 2.4.1.5. Mixed Pooling2.4.1.6. Multiscale Orderless Pooling; 2.4.1.7. Spectral Pooling; 2.4.2. Types of Nonlinear Functions; 2.4.2.1. Sigmoid Neurons; 2.4.2.2. Maxout Neurons; 2.4.2.3. Rectified Linear Units; 2.4.2.4. Parameterized Rectified Linear Units; 2.4.2.5. Dropout; 2.5. CNN-Based End-to-End Approach; 2.6. Experiments and Their Results; 2.7. Conclusion; References; Chapter 3: A Real-Time DSP-Based System for Voice Activity Detection and Background Noise Reduction; 3.1. Introduction; 3.2. Microchip dsPIC33 Digital Signal Controller; 3.2.1. VAD and Noise Suppression Algorithm 3.3. High Pass Filter3.4. 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; References; Further Reading; Chapter 4: Disambiguating Conflicting Classification Results in AVSR 4.1. Introduction4.2. Detection of Conflicting Classes; 4.3. Complementary Models for Classification; 4.4. Proposed Cascade of Classifiers; 4.5. Audio-Visual Databases; 4.5.1. AV-CMU Database; 4.5.2. AV-UNR Database; 4.5.3. AVLetters Database; 4.6. Experimental Results; 4.6.1. Hidden Markov Models; 4.6.2. Random Forest; 4.6.3. Support Vector Machine; 4.6.4. AdaBoost; 4.6.5. Analysis and Comparison; 4.7. Conclusions; References; Chapter 5: A Deep Dive Into Deep Learning Techniques for Solving Spoken Language Identification Problems; 5.1. Introduction; 5.2. Spoken Language Identification 5.3. Cues for Spoken Language Identification5.4. 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; References; Chapter 6: Voice Activity Detection-Based Home Automation System for People With Special Needs; 6.1. Introduction; 6.2. Conceptual Design of the System; 6.3. System Implementation; 6.3.1. Speech Recognition; 6.3.2. System Automation; 6.3.3. Other Applications; 6.3.4. Results and Discussion
Responsibility: edited by Nilanjan Dey.

Abstract:

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.

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