Ljubomir Josifovski

TL;DR: An approach to robust ASR which acknowledges the fact that some spectro-temporal regions will be dominated by noise, and introduces two approaches for dealing with unreliable evidence, including marginalisation and state-based data imputation.

TL;DR: The theory and promise of the Missing Data approach to robust Automatic Speech Recognition is developed and the probability calculation is adapted to use these estimates as weighting factors for the complementary reliable/unreliable interpretations for each feature vector component.

TL;DR: This paper addresses the problem of identifying reliable regions and proposes two criteria to solve this based on negative energy and SNR and shows that in this task the missing data method performs considerably better than spectral subtraction and the combination of the two techniques outperforms either technique used alone.

TL;DR: A formalism for data imputation based on the probability distributions of individual Hidden Markov model states is presented and potential advantages are that it can be followed by conventional techniques like cepstral features or artificial neural networks for speech recognition.

TL;DR: This work,techniques forrobustautomaticsp eechrecognition(ASR)typicallyaimfornear-p erfectallo-cationoftheacousticmixtureintoadditivecontribu-tions fromconstituentsources(seeFurui(1997) forareview), makinguse ofnoisesourcemo dels.