Armin Saeb

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Although exemplar based approaches have shown good accuracy in classification problems, some limitations are observed in the accuracy of exemplar based automatic speech recognition (ASR) applications. The main limitation of these algorithms is their high computational complexity which makes them difficult to extend to ASR applications. In this paper, an(More)
In this paper, a new fast compressive sensing (CS) algorithm for phoneme classification is introduced. In this approach, unlike common CS classification approaches that use CS as a classifier, we use CS as an N-best class selector to limit the secondary classifier input into certain classes. In addition, we use a tree search strategy to select most similar(More)
We consider the extraction of information from broadcast radio speech in Uganda for the purposes of informing relief and development programmes by the United Nations. Although internet penetration in Uganda is low, mobile phones are ubiquitous and have made radio a vibrant medium for interactive public discussion. Vulnerable groups make use of radio to(More)
We present a radio browsing system developed on a very small corpus of annotated speech by using semi-supervised training of multilingual DNN/HMM acoustic models. This system is intended to support relief and developmental programmes by the United Nations (UN) in parts of Africa where the spoken languages are extremely under resourced. We assume the(More)
In this paper, an N-best class selector based on compressive sensing (CS) algorithm and a tree search strategy is introduced and applied for classification applications and its accuracy and complexity are compared with some well-known classifiers. In this approach, classification is done in three steps. At first, the set of most similar training samples for(More)
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