Today, a large number of audio features exists in audio retrieval for different purposes, such
as automatic speech recognition, music information retrieval, audio segmentation, and …

In this paper, we use concepts and methods from chaotic systems to model and analyze
nonlinear dynamics in speech signals. The modeling is done not on the scalar speech …

We explore nonlinear signal processing methods inspired by dynamical systems and fractal
theory in order to analyze and characterize speech sounds. A speech signal is at first …

Early onset ataxia represents a group of heterogeneous neurological conditions typically
characterized by motor disability. Speech problems are one of the main core features of …

Mel frequency cepstral coefficients (MFCCs) are a standard tool for automatic speech
recognition (ASR), but they fail to capture part of the dynamics of speech. The nonlinear …

In this paper we propose the use of nonlinear speech features to improve the voice quality
measurement. We have tested a couple of features from the Dynamical System Theory …

In this paper, a feature extraction (FE) method is proposed that is comparable to the
traditional FE methods used in automatic speech recognition systems. Unlike the …

Investigating new effective feature extraction methods applied to the speech signal is an
important approach to improve the performance of automatic speech recognition (ASR) …

Nonlinear properties of a complex signal can be represented in reconstructed phase space
(RPS). Previously, researchers have developed RPS-based feature extraction approaches …

Perhaps the first question to ask on reading this chapter is why should we consider
nonlinear methods as offering any insight into speech signals given the success of current …