Applied Speech Processing: Algorithms and Case Studies is concerned with supporting and enhancing the utilization of speech analytics in several systems and real-world activities, including sharing data analytics related information, creating collaboration networks between several participants, and the use of video-conferencing in different application areas. The book provides a well-standing forum to discuss the characteristics of the intelligent speech signal processing systems in different domains. The book is proposed for professionals, scientists, and engineers who are involved in new techniques of intelligent speech signal processing methods and systems. It provides an outstanding foundation for undergraduate and post-graduate students as well.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. ix 7. Preface - p. xi 8. Part 1: Speech enhancement and synthesis - p. xii 9. Chapter 1. Kurtosis-based, data-selective affine projection adaptive filtering algorithm for speech processing application - p. 3 10. Chapter 2. Recursive noise estimation-based Wiener filtering for monaural speech enhancement - p. 27 11. Chapter 3. Modified least mean square adaptive filter for speech enhancement - p. 60 12. Chapter 4. Unsupervised single-channel speech enhancement based on phase aware time-frequency mask estimation - p. 75 13. Chapter 5. Harmonic adaptive speech synthesis - p. 101 14. Part 2: Speech identification, feature selection and classification - p. 114 15. Chapter 6. Linguistically involved data-driven approach for Malayalam phoneme-to-viseme mapping - p. 135 16. Chapter 7. Closed-set speaker identification system based on MFCC and PNCC features combination with different fusion strategies - p. 147 17. Chapter 8. Analysis of machine learning algorithms for audio event classification using Mel-frequency cepstral coefficients - p. 175 18. Index - p. 191
Applied Speech Processing: Algorithms and Case Studies is concerned with supporting and enhancing the utilization of speech analytics in several systems and real-world activities, including sharing data analytics related information, creating collaboration networks between several participants, and the use of video-conferencing in different application areas. The book provides a well-standing forum to discuss the characteristics of the intelligent speech signal processing systems in different domains. The book is proposed for professionals, scientists, and engineers who are involved in new techniques of intelligent speech signal processing methods and systems. It provides an outstanding foundation for undergraduate and post-graduate students as well.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Contributors - p. ix 7. Preface - p. xi 8. Part 1: Speech enhancement and synthesis - p. xii 9. Chapter 1. Kurtosis-based, data-selective affine projection adaptive filtering algorithm for speech processing application - p. 3 10. Chapter 2. Recursive noise estimation-based Wiener filtering for monaural speech enhancement - p. 27 11. Chapter 3. Modified least mean square adaptive filter for speech enhancement - p. 60 12. Chapter 4. Unsupervised single-channel speech enhancement based on phase aware time-frequency mask estimation - p. 75 13. Chapter 5. Harmonic adaptive speech synthesis - p. 101 14. Part 2: Speech identification, feature selection and classification - p. 114 15. Chapter 6. Linguistically involved data-driven approach for Malayalam phoneme-to-viseme mapping - p. 135 16. Chapter 7. Closed-set speaker identification system based on MFCC and PNCC features combination with different fusion strategies - p. 147 17. Chapter 8. Analysis of machine learning algorithms for audio event classification using Mel-frequency cepstral coefficients - p. 175 18. Index - p. 191
