In this contribution a modified scheme for linear prediction analysis is presented which controls the degree of decorrelation by a parameter α. In consideration of this parameter the signal to noise ratio of a linear predictive coding scheme is investigated and finally maximized for open loop quantization. Also, the new parameter can be used to control the spectral shape of the quantization err...

Identical twins pose a great challenge to face recognition systems due to their similar appearance. Nevertheless, even though twins may look alike, we believe they speak differently. Hence we propose to use their voice patterns to distinguish between twins. Voice is a natural signal to produce, and it is a combination of physiological and behavioral biometrics, therefore it is suitable for twin...

Psychoacoustical models have been used extensively within audio coding applications over the past decades. Recently, parametric coding techniques have been applied to general audio and this has created the need for a psychoacoustical model that is specifically suited for sinusoidal modelling of audio signals. In this paper, we present a new perceptual model that predicts masked thresholds for s...

Production of vowel–oral stop consonant–vowel utterances involves movement of articulators from the articulatory position of the initial vowel towards that of the oral stop closure, and then to that of the final vowel. As the closure segments have zero or low signal energy, linear predictive coding (LPC)-based estimation of vocal tract shape fails during stop closure. This paper reports a techn...

An automated speech signal labeling tool, developed for the QuickSig speech database environment, is described. It is based primarily on the use of neural networks as diphone event detectors. For robustness, only coarse categories of diphones, such as stop–vowel and vowel–nasal, are used. 64 such detectors are implemented to cover all of the Finnish diphones. The preprocessing of speech signals...

In this study, modulation spectral features (MSFs) are proposed for the automatic recognition of human affective information from speech. The features are extracted from an auditory-inspired long-term spectro-temporal representation. Obtained using an auditory filterbank and a modulation filterbank for speech analysis, the representation captures both acoustic frequency and temporal modulation ...

Earlier work in parametric modeling of distortions for robust speech recognition has focussed on estimating the distortion parameter using maximum likelihood and other techniques as a point in the parameter space, and treating this estimate as if it is the true value in a plug-in maximum a posteriori (MAP) decoder. This approach is deficient in most real environments where, due to many reasons,...

MOTIVATION Microarrays are being increasingly used in cancer research to better characterize and classify tumors by selecting marker genes. However, as very few of these genes have been validated as predictive biomarkers so far, it is mostly conventional clinical and pathological factors that are being used as prognostic indicators of clinical course. Combining clinical data with gene expressio...

In this paper, a novel solving method for speech signal chaotic time series prediction model was proposed. A phase space was reconstructed based on speech signal’s chaotic characteristics and the genetic programming (GP) algorithm was introduced for solving the speech chaotic time series prediction models on the phase space with the embedding dimension m and time delay . And then, the speech si...

A possibility to use the formant features (FF) in the user-dependent isolated word recognition has been investigated. The word recognition was performed using a dynamic time-warping technique. Several methods of the formant feature extraction were compared and a method based on the singular prediction polynomials has been proposed for the recognition of isolated words. Recognition performance o...