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2014 | Book

Introduction Read chapter Read first chapter

Application of Wavelets in Speech Processing

Author: Mohamed Hesham Farouk

Publisher: Springer International Publishing

Book Series : SpringerBriefs in Electrical and Computer Engineering

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

This book provides a survey on wide-spread of employing wavelets analysis in different applications of speech processing. The author examines development and research in different applications of speech processing. The book also summarizes the state of the art research on wavelet in speech processing.

Table of Contents

Frontmatter
Chapter 1. Introduction
Abstract
As the wavelets gain wide applications in different fields, especially within the signal processing realm, this chapter will provide a survey on widespread employing of wavelets analysis in different applications of speech processing. Many speech processing algorithms and techniques still lack some sort of robustness which can be improved through the use of wavelet tools. Researchers and practitioners in speech technology will find valuable information on the use of wavelets to strengthen both development and research in different applications of speech processing.
Mohamed Hesham Farouk
Chapter 2. Speech Production and Perception
Abstract
The main objective of research in speech processing is directed toward finding techniques for extracting features, which robustly model a speech signal. Some of these features can be characterized by relatively simple models, while others may require more realistic models in both cases of speech production and perception.
Mohamed Hesham Farouk
Chapter 3. Wavelets, Wavelet Filters, and Wavelet Transforms
Abstract
Spectral characteristics of speech are known to be particularly useful in describing a speech signal such that it can be efficiently reconstructed after coding or identified for recognition. Wavelets are considered one of such efficient methods for representing the spectrum of speech signals. Wavelets are used to model both production and perception processes of speech. Wavelet-based features prove a success in a widespread area of practical applications in the speech processing realm.
Mohamed Hesham Farouk
Chapter 4. Speech Enhancement and Noise Suppression
Abstract
Wavelet analysis has been widely used for noise suppression in signals. The multiresolution properties of wavelet analysis reflect the frequency resolution of the human ear. The wavelet transform (WT) can be adapted to distinguish noise in speech through its properties in the time and frequency domains.
Mohamed Hesham Farouk
Chapter 5. Speech Quality Assessment
Abstract
The wavelet packet analysis can be used to improve a perceptual-based objective speech quality measure. In this measure, the critical bands of auditory system can be approximated by a predefined wavelet packet (PWP) tree structure.
Mohamed Hesham Farouk
Chapter 6. Speech Recognition
Abstract
Wavelet analysis can be used to improve the speech recognition performance through two approaches. In the first approach, it can be used as the back-end to remove noise and consequently the recognition process may perform better. In the second approach, wavelet-based features can be added to other successful features to improve recognition performance.
Mohamed Hesham Farouk
Chapter 7. Emotion Recognition from Speech
Abstract
Like speech recognition, emotion recognition can benefit from the merits of wavelet analysis in feature extraction or mapping functions.
Mohamed Hesham Farouk
Chapter 8. Speaker Recognition
Abstract
MFCC features are widely used in speaker recognition. However, MFCC is not suitable for identifying a speaker since they should be located in high frequency regions, while the Mel scale gets coarser in the higher frequency bands. The speaker individual information, which is nonuniformly distributed in the high frequencies, is equally important for speaker recognition; accordingly, wavelet-based features are more appropriate than MFCC.
Mohamed Hesham Farouk
Chapter 9. Spectral Analysis of Speech Signal and Pitch Estimation
Abstract
Wavelet transform (WT) provides a way to explore the characteristics of nonstationary speech signals. Both time and frequency analysis can be conducted by WT. The tree structure of WP analysis can be customized to the critical bands of human hearing giving better spectral estimation for speech signal than other methods. Wavelet-based pitch estimation assumes that the glottis closures are correlated with the maxima in the adjacent scales of the WT. This approach ensures more accurate estimation of pitch period.
Mohamed Hesham Farouk
Chapter 10. Speech Coding, Synthesis, and Compression
Abstract
WT-based coding allows for the control of frequency resolution to closely match the response of the human auditory system. The inherent shaping of the wavelet synthesis filter and a controlled bit allocation to the wavelet coefficients help to minimize the perceptually significant noise due to the quantization error in the residual. Experimental results show that WT-coders deliver superior quality to some audio standards when operating at the same bit rate and comparable quality to other codecs at lower bit rates. As a result, transform compression using WT can provide an efficient and flexible scheme for audio compression.
Mohamed Hesham Farouk
Chapter 11. Speech Detection and Separation
Abstract
Many methods which are used for speech detection usually fail when signal-to-noise ratio (SNR) is low. The wavelet analysis has properties which can help in separating speech from noise. Many works report a better detection performance using wavelet analysis than other techniques.
Mohamed Hesham Farouk
Chapter 12. Steganography and Security of Speech Signal
Abstract
Perfect reconstruction of wavelet filter banks helps in retrieving a hidden signal. In wavelet domain different techniques are applied on the wavelet coefficients to increase the hiding capacity and perceptual transparency. In general, steganography in wavelet domain shows high hiding capacity and transparency.
Mohamed Hesham Farouk
Chapter 13. Clinical Diagnosis and Assessment of Speech Disorders
Abstract
WT coefficients for normal voiced signal have remarkable differences compared to pathological ones. Accordingly, WT is successfully used as a noninvasive method to diagnose vocal pathologies.
Mohamed Hesham Farouk
Backmatter
Metadata
Title
Application of Wavelets in Speech Processing
Author
Mohamed Hesham Farouk
Copyright Year
2014
Electronic ISBN
978-3-319-02732-6
Print ISBN
978-3-319-02731-9
DOI
https://doi.org/10.1007/978-3-319-02732-6