Preface

Chapter 6 **Face Recognition Based on Texture Descriptors 111**

Chapter 7 **Learning Algorithms for Fuzzy Inference Systems Using Vector**

Chapter 8 **Query Morphing: A Proximity-Based Approach for Data**

Chapter 9 **Cellular Automata and Randomization: A Structural**

Chapter 10 **Hard, firm, soft … Etherealware:Computing by Temporal Order**

Hirofumi Miyajima, Noritaka Shigei and Hiromi Miyajima

Carlos Castro-Madrid

**VI** Contents

**Quantization 129**

**Exploration 147**

**Overview 165** Monica Dascălu

**of Clocking 185** Michael Vielhaber

Jay Patel and Vikram Singh

**Section 5 Cellular Automata Applications 163**

**Section 4 Fuzzy Inference and Data Exploration 127**

Jesus Olivares-Mercado, Karina Toscano-Medina, Gabriel Sanchez-Perez, Mariko Nakano Miyatake, Hector Perez-Meana and Luis

> Artificial intelligence (AI) is a field experiencing constant growth and change, with a long history. The challenge to reproduce human behavior in machines requires the interaction of many fields, from engineering to mathematics, from neurology to biology, from computer science to robotics, from web search to social networks, from machine learning to game theo‐ ry, etc. Numerous applications and possibilities of AI are already a reality but other ones are needed to reduce the human limitations and to expand the human capability to limits beyond our imagination. This book brings together researchers working on areas related to AI such as face and speech recognition, representation of learning and acoustic scenarios, fuzzy infer‐ ence and data exploration, cellular automata applications with a special interest in the tools and algorithms that can be applied in these different branches of the AI discipline. The book provides a new reference to an audience interested in the development of this field.

> The first three sections of the book present different algorithms and models with a variety of applications in speech and face recognition and also in learning and acoustic scenarios. In Chapter 1, Alim and Rashid explain some extraction techniques used for speech identification and recognition. In Chapter 2, Passricha and Aggarwal discuss an acoustic model based on convolutional neural networks to decode raw speech signals. In Chapter 3, Llorca et al. evalu‐ ate the subjective impact of immersive acoustic features in the representation of urban envi‐ ronments. In Chapter 4, Morales-Martínez et al. present tools of cognitive science to improve adaptive e-learning systems. In Chapter 5, Lin discusses local pattern descriptors for face rec‐ ognition. The last chapter of this section, Chapter 6 by Olivares-Mercado et al., analyzes the performance of different texture descriptor algorithms for face feature extraction tasks.

> The last two sections of the book present some investigations in fuzzy inference and data exploration and also different cellular automata applications. In Chapter 7, Miyajima et al. study the improvement of different fuzzy methods for pattern classification. In Chapter 8, Patel and Singh propose a query reformulation method for a relevant data exploration. In Chapter 9, Dascalu discusses algorithms and architectures of cellular automata used as ran‐ dom number generators. In the last chapter, Chapter 10, Vielhaber studies the randomness in cellular automata evolving under asynchronous clocking schemes.

> As the editor of this book, I would like to thank all the authors who have contributed to this volume as well as the reviewers for their assessment. Also, I must express my gratitude to the IntechOpen Editorial Staff for their invitation to be editor for a third time, and Author Service Managers (ASM) Mr. Markus Mattila and Ms. Ljerka Bilan, who were both of partic‐ ular help in realizing this new IntechOpen book. Finally, at this moment where life is a sweet time flow, I want to dedicate this book to my very good friend Enrique Lozano Corbi, recently retired from his position as Professor of Roman Law after forty-four years working at the University of Zaragoza. Of course, all my family from Villafranca, Navarra, Spain, and all my friends and advisors are not forgotten in this dedicatory final paragraph.

> > **Ricardo López-Ruiz** University of Zaragoza, Spain

**Section 1**

**Speech Extraction and Recognition**

**Speech Extraction and Recognition**

**Chapter 1**

Provisional chapter

**Some Commonly Used Speech Feature Extraction**

DOI: 10.5772/intechopen.80419

Speech is a complex naturally acquired human motor ability. It is characterized in adults with the production of about 14 different sounds per second via the harmonized actions of roughly 100 muscles. Speaker recognition is the capability of a software or hardware to receive speech signal, identify the speaker present in the speech signal and recognize the speaker afterwards. Feature extraction is accomplished by changing the speech waveform to a form of parametric representation at a relatively minimized data rate for subsequent processing and analysis. Therefore, acceptable classification is derived from excellent and quality features. Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), Line Spectral Frequencies (LSF), Discrete Wavelet Transform (DWT) and Perceptual Linear Prediction (PLP) are the speech feature extraction techniques that were discussed in these chapter. These methods have been tested in a wide variety of applications, giving them high level of reliability and acceptability. Researchers have made several modifications to the above discussed techniques to make them less susceptible to noise, more robust and consume less time. In conclusion, none of the methods is superior to the other, the area of application would

Keywords: human speech, speech features, mel frequency cepstral coefficients (MFCC), linear prediction coefficients (LPC), linear prediction cepstral coefficients (LPCC), line spectral frequencies (LSF), discrete wavelet transform (DWT), perceptual linear prediction

Human beings express their feelings, opinions, views and notions orally through speech. The speech production process includes articulation, voice, and fluency [1, 2]. It is a complex

> © 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

Some Commonly Used Speech Feature Extraction

Sabur Ajibola Alim and Nahrul Khair Alang Rashid

Sabur Ajibola Alim and Nahrul Khair Alang Rashid

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.80419

determine which method to select.

**Algorithms**

Abstract

(PLP)

1. Introduction

Algorithms

#### **Some Commonly Used Speech Feature Extraction Algorithms** Some Commonly Used Speech Feature Extraction Algorithms

DOI: 10.5772/intechopen.80419

Sabur Ajibola Alim and Nahrul Khair Alang Rashid Sabur Ajibola Alim and Nahrul Khair Alang Rashid

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.80419

#### Abstract

Speech is a complex naturally acquired human motor ability. It is characterized in adults with the production of about 14 different sounds per second via the harmonized actions of roughly 100 muscles. Speaker recognition is the capability of a software or hardware to receive speech signal, identify the speaker present in the speech signal and recognize the speaker afterwards. Feature extraction is accomplished by changing the speech waveform to a form of parametric representation at a relatively minimized data rate for subsequent processing and analysis. Therefore, acceptable classification is derived from excellent and quality features. Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), Line Spectral Frequencies (LSF), Discrete Wavelet Transform (DWT) and Perceptual Linear Prediction (PLP) are the speech feature extraction techniques that were discussed in these chapter. These methods have been tested in a wide variety of applications, giving them high level of reliability and acceptability. Researchers have made several modifications to the above discussed techniques to make them less susceptible to noise, more robust and consume less time. In conclusion, none of the methods is superior to the other, the area of application would determine which method to select.

Keywords: human speech, speech features, mel frequency cepstral coefficients (MFCC), linear prediction coefficients (LPC), linear prediction cepstral coefficients (LPCC), line spectral frequencies (LSF), discrete wavelet transform (DWT), perceptual linear prediction (PLP)
