Dimitrios Dimitriadis

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In this paper, a feature extraction algorithm for robust speech recognition is introduced. The feature extraction algorithm is motivated by the human auditory processing and the nonlinear Teager-Kaiser energy operator that estimates the true energy of the source of a resonance. The proposed features are labeled as Teager Energy Cepstrum Coefficients(More)
Purpose: Our objective was to apply ooplasmic round spermatid nuclear injections for the treatment of nonobstructive azoospermia. Materials: Participants were nine azoospermic men who had previously undergone diagnostic testicular biopsy. Spermatogenetic arrest was diagnosed at the round spermatid stage (n=6) or primary spermatocyte stage (n=3). A second(More)
In this letter, a nonlinear AM-FM speech model is used to extract robust features for speech recognition. The proposed features measure the amount of amplitude and frequency modulation that exists in speech resonances and attempt to model aspects of the speech acoustic information that the commonly used linear source-filter model fails to capture. The(More)
Time-frequency distributions that evaluate the signal's energy content both in the time and frequency domains are indispensable signal processing tools, especially, for nonstationary signals. Various short-time energy computation schemes are used in practice, including the mean squared amplitude and Teager-Kaiser energy approaches. Herein, we focus(More)
Word error rates on the Switchboard conversational corpus that just a few years ago were 14% have dropped to 8.0%, then 6.6% and most recently 5.8%, and are now believed to be within striking range of human performance. This then raises two issues: what is human performance, and how far down can we still drive speech recognition error rates? In trying to(More)
We propose a novel Automatic Speech Recognition (ASR) front-end, that consists of the first central Spectral Moment time-frequency distribution Augmented by low order Cepstral coefficients (SMAC). We prove that the first central spectral moment is proportional to the spectral derivative with respect to the filter's central frequency. Consequently, the(More)
In this paper, we develop improved schemes for simultaneous speech interpolation and demodulation based on continuous-time models. This leads to robust algorithms to estimate the instantaneous amplitudes and frequencies of the speech resonances and extract novel acoustic features for ASR. The continous-time models retain the excellent time resolution of the(More)
This paper proposes a post-filtering estimation scheme for multichannel noise reduction. The proposed method extends and improves the existing Zelinski’s and, the most general and prominent, McCowan’s post-filtering methods that use the autoand crossspectral densities of the multichannel input signals to estimate the transfer function of the Wiener(More)
The problem of speaker and channel adaptation in deep neural network (DNN) based automatic speech recognition (ASR) systems is of substantial interest in advancing the performance of these systems. Recently, the speaker identity vectors (i-vectors) have shown improvements for ASR systems in matched conditions. In this paper, we propose the application of(More)
Speech resonance signals appear to contain significant amplitude and frequency modulations. An efficient demodulation approach is based on energy operators. In this paper, we develop two new robust methods for energy-based speech demodulation and compare their performance on both test and actual speech signals. The first method uses smoothing splines for(More)