Ronan Flynn

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The performance of Automatic Speech Recognition (ASR) systems in the presence of noise is an area that has attracted a lot of research interest. Additive noise from interfering noise sources, and convolutional noise arising from transmission channel characteristics both contribute to a degradation of performance in ASR systems. This paper addresses the(More)
This thesis describes a representation for objects and scenes that is stable against variations in image intensity caused by illumination changes and tolerant to image degradations such as sensor noise. The representation, called a ratio-template, uses low-resolution ordinal contrast relationships as its matching primitives. The choice of these primitives(More)
a r t i c l e i n f o a b s t r a c t This paper examines the performance of a Distributed Speech Recognition (DSR) system in the presence of both background noise and packet loss. Recognition performance is examined for feature vectors extracted from speech using a physiologically-based auditory model, as an alternative to the more commonly-used Mel(More)
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