Learn More
Robustness of classification of isolated phoneme segments using discriminative and generative classifiers is investigated for the acoustic waveform and PLP speech representations. The two approaches used are support vector machines (SVMs) and mixtures of probabilistic PCA (MPPCA). While recognition in the PLP domain attains superb accuracy on clean data, it(More)
SUMMARY Loss exponential transmission line as impedance transformer, with capacitance per unit length approximated by power series, is considered in this paper. The values of input impedance transformed through exponential and the considered transmission line are presented. Dependence of input impedance at arbitrary distance along the approximated line, its(More)
Phoneme classification in frequency bands of acoustic waveforms is studied. The goal is to investigate whether separate classifications across a number of subband signals, combined using appropriate machine learning algorithms, can provide performance similar to classification performed directly on the original acoustic waveforms. If this is the case, then(More)
Automatic speech recognition (ASR) systems are yet to achieve the level of robustness inherent to speech recognition by the human auditory system. The primary goal of this paper is to argue that exploiting the redundancy in speech signals could be the key to solving the problem of the lack of robustness. This view is supported by our recent results on(More)
  • 1