An objective distance measure which is able to predict audible discontinuity in concatenated speech synthesis systems is very important. Previous works were primarily based on features estimated by linear and/or stationary models of speech. In this paper, we introduce two nonlinear approaches for the detection of discontinuity. The first method is based on a nonlinear harmonic model of speech w...

Instantaneous frequency (IF) is necessary for understanding the detailed mechanisms for nonlinear and nonstationary processes. Historically, IF was computed from analytic signal (AS) through the Hilbert transform. This paper offers an overview of the difficulties involved in using AS, and two new methods to overcome the difficulties for computing IF. The first approach is to compute the quadrat...

It is well known that the performance of speech recognition algorithms is greatly innuenced by stressful conditions in which speech is produced. It is suggested that algorithms which are capable of classifying stress could be beneecial in the formulation of more eeective speech recognition algorithms under stressful conditions. Recently , three nonlinear features derived from Teager Energy Oper...

Stress detection from speech is a less explored field than Automatic Emotion Recognition and it is still not clear which features are better stress discriminants. VOCE aims at doing speech classification as stressed or not-stressed in real-time, using acoustic-prosodic features only. We therefore look for the best discriminating feature subsets from a set of 6285 features – 6125 features extrac...

In this paper we present a novel approach for the automatic detection of clicks from recordings of beaked whales, based on the phase characteristics of minimum phase signals and especially using the group delay function. Group delay is estimated through the direct and first derivative of the Fourier Transform of a signal. A major advantage of the proposed approach is its robustness against addi...

In this paper we study a non-linear diffusion process to reduce the influence of noise in the watershed segmentation of an image. Instead of the squared amplitude of the gradient that is traditionally used to drive the non-linear diffusion, we use the Teager energy, which is known to be less sensitive to noise. To evaluate the performance of the segmentation processes studied in this paper, we ...

In this paper we explore the use of features derived from multiresolution analysis of speech and the Teager Energy Operator for classi cation of drivers' speech under stressed conditions. We apply this set of features to a database of short speech utterances to create userdependent discriminants of four stress categories. In addition we address the problem of choosing a suitable temporal scale ...

*This research was funded by DARPA through SPAWAR under Grant No. N ABSTRACT In this paper we explore the use of nonlinear Teager Energy Operator based features derived from multiresolution sub-band analysis for classification of emotional/stressful speech. We propose a novel scheme for automatic sub-band weighting in an effort towards developing a generic algorithm for understanding emotion or...

In this paper, the analysis of low-frequency zone of the speech signals from the five Spanish vowels, by means of the Teager energy operator (TEO) and the modified group delay functions (MGDF) is proposed for the automatic detection of Parkinson’s disease. According to our findings, different implementations of the TEO are suitable for tackling the problem of the automatic detection of Parkinso...

We present in this paper a novel and efficient technique for the restoration of color images which are highly corrupted with impulse noise. This is a detectionestimation based approach in which outliers are first detected using a Teager-like operator followed by a locally adaptive threshold. Center pixels whose “energy” exceeds some threshold are replaced with the local marginal median. Simulat...