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Intelligent speech signal processing /
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....
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Academic Press,
[2019]
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| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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