Brook charr (Salvelinus fontinalis) is a sentinel fish species that requires clean, cold water habitats generally resulting from landscapes that allow for surface water flows devoid of sediment and contaminants and high groundwater discharge of cold water. As such, brook charr are impacted by land cover changes that alter stream temperature regimes. We evaluated brook charr populations across their eastern and midwestern range in the United States with reference to thermal habitat availability in relationship to land cover and per cent baseflow. We found that while forest cover does protect brook charr thermal habitat, high levels of groundwater discharge can allow for increased levels of agriculture within a watershed by keeping the water cold in spite of warm ambient summer temperatures. Our study concludes that with enhanced communication among land, water and fisheries managers, society can provide for sustainable stream salmonid populations despite increased threats on cold water resources.
This paper addresses the issue of automatic word/sentence boundary detection in both quiet and noisy environments. We propose to use an entropy based contrast function between the speech segments and the background noise. A simplified data based scheme of computing the entropy of the speech data is presented. The entropy-based contrast exhibits better-behaved characteristics as compared to the energy-based methods. An adaptive threshold is used to determine the candidate speech segments, which are subjected to word/sentence constraints. Experimental results show that this algorithm outperforms energy-based algorithms. The improved detection accuracy of speech segments results in at least 25 % improvement of recognition performance for isolated speech and more than 16% for connected speech. For continuous speech, a preprocessing stage comprising of the proposed speech segment detection makes the overall HMM based scheme more computationally efficient by rejection of silence periods.
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