Acknowledgement Acknowledgement I would first like to thank my advisor, Dr. Richard Povinelli, whose advice and support have been critical in the development of this work. My thanks also go to Dr. Michael Johnson, whose expertise has been very valuable to me. I also thank my committee members, Drs. George Corliss, Craig Struble, and Edwin Yaz for all their assistance. Thanks go to Dr. Mohamed M...

This paper presents a set of cepstral parameters based on timevarying linear prediction. The lattice filter structure is utilized to accommodate efficient stabilization of models and a Bark-like warped frequency scale. As the proposed cepstral features are based on non-stationary spectral analysis there is a potential for complementary information not captured in conventional features. In class...

Speech recognition is of an important contribution in promoting new technologies in human computer interaction. Today, there is a growing need to employ speech technology in daily life and business activities. However, speech recognition is a challenging task that requires different stages before obtaining the desired output. Among automatic speech recognition (ASR) components is the feature ex...

In this paper, a system for pitch independent musical instrument recognition is presented. A wide set of features covering both spectral and temporal properties of sounds was investigated, and their extraction algorithms were designed. The usefulness of the features was validated using test data that consisted of 1498 samples covering the full pitch ranges of 30 orchestral instruments from the ...

This paper describes a robust feature extraction technique for continuous speech recognition. Central to the technique is the Minimum Variance Distortionless Response (MVDR) method of spectrum estimation. We incorporate perceptual information directly in to the spectrum estimation. This provides improved robustness and computational efficiency when compared with the previously proposed MVDR-MFC...

In the present study, a new feature extraction method based on relative spectra and gammachirp auditory filterbank is proposed for robust noisy speech recognition. The relative spectra filtering are applied to the log of the output of the gammachirp filterbank which incorporates the properties of the cochlear filter in order to remove uncorrelated additive noise components. The performances of ...

The objective of the presentation is to report experiments involving the automatic classification of disordered connected speech into binary (normal, pathological) or multiple (modal, moderately hoarse, severely hoarse) categories. The multicategory classification according to the perceived degree of hoarseness is considered to be clinically meaningful and desirable given that the reliable perc...

Mel-frequency cepstral coefficients are used as an abstract representation of the spectral envelope of a given signal. Although they have been shown to be a powerful descriptor for speech and music signals, more accurate and easily interpretable options can be devised. In this study, we present and evaluate the shape-based spectral contrast descriptor, which is build up from the previously prop...

The intelligibility of syllables whose cepstral trajectories were temporally filtered was measured. The speech signals were transformed to their LPC cepstral coefficients, and these coefficients were passed through different filters. These filtered trajectories were recombined with the residuals and the speech signal reconstructed. The intelligibility of the reconstructed speech segments was th...

Cepstral coefficients are widely used in speech recognition. In this paper, we claim that they are not the best way of representing the spectral envelope, at least for some usual speech recognition systems. In fact, cepstrum has several disadvantages: poor physical meaning, need of transformation, and low capacity of adaptation to some recognition systems. In this paper, we propose a new repres...