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Intelligent speech signal processing

Dey, Nilanjan, (1984-)

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Intelligent speech signal processing

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Intelligent speech signal processing/ edited by Nilanjan Dey.
其他作者:	Dey, Nilanjan,
出版者:	London :Academic Press, : 2019.,
面頁冊數:	1 online resource :ill. (some col.)
內容註:	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
內容註:	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
內容註:	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
內容註:	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
內容註:	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
標題:	Automatic speech recognition. -
電子資源:	https://www.sciencedirect.com/science/book/9780128181300
ISBN:	9780128181317 (electronic bk.)

Intelligent speech signal processing
Intelligent speech signal processing[electronic resource] /edited by Nilanjan Dey. - First edition. - London :Academic Press,2019. - 1 online resource :ill. (some col.)

Includes bibliographical references and index.

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

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.

ISBN: 9780128181317 (electronic bk.)Subjects--Topical Terms:

753709
Automatic speech recognition.
Index Terms--Genre/Form:

542853
Electronic books.

LC Class. No.: TK7895.S65

Dewey Class. No.: 006.454

Intelligent speech signal processing
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505 0 $a 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
505 8 $a 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
505 8 $a 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
505 8 $a 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
505 8 $a 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
520 $a 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.
588 0 $a Online resource; title from PDF title page (EBSCO, viewed March 29, 2019).
650 0 $a Automatic speech recognition. $3 753709
650 0 $a Speech processing systems. $3 590570
650 0 $a Signals and signaling. $3 625187
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700 1 $a Dey, Nilanjan, $d 1984- $e editor. $3 3231517
856 4 0 $u https://www.sciencedirect.com/science/book/9780128181300