References AITKEN, S. N., S. YEAMAN, J. A. HOLLIDAY, T. L WANG and S. CURTIS-MCLANE (2008): Adaptation, migration or extirpation: climate change outcomes for tree populations. Evolutionary Applications 1: 95-111. ALÍA, R., T. M. ROBSON, G. BOZ˘IC˘, D. GÖMÖRY, G. HUBER, A. DUCOUSSO, E. RASZTOVITS and G. VON WÜHLISCH (2011): The survival and performance of beech provenances over a Europe-wide gradient of climate. Serie Forestal, INIA, Madrid 22: 115-126. ANDALO, C., J BEAULIEU and J. BOUSQUET (2005): The impact of
V. Foff, F. Weiser, E. Foffová and Dušan Gömöry
Roman Babko, Tatiana Kuzmina, Katarzyna Jaromin-Gleń and Andrzej Bieganowski
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Sumarmi and Purwanto
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Chosen problems of law implementation in the contemporary process of building’s modernization in Poland.
One of the major problems in the contemporary process of building’s modernization in Poland is the pluralism of different interpretations of chosen legal terms, existing in the contemporary building code. Incorrect interpretation, results in the incorrect application to the authorities for the proper building permit and as the effect, it causes the lost of time and money. The article tries to identify some of these problems and seeks the solution to solve them, through the evolutionary method of building law creation.
Gábor Gosztolya and Tamás Grósz
Wireless sensors are recent, portable, low-powered devices, designed to record and transmit observations of their environment such as speech. To allow portability they are designed to have a small size and weight; this, however, along with their low power consumption, usually means that they have only quite basic recording equipment (e.g. microphone) installed. Recent speech technology applications typically require several dozen hours of audio recordings (nowadays even hundreds of hours is common), which is usually not available as recorded material by such sensors. Since systems trained with studio-level utterances tend to perform suboptimally for such recordings, a sensible idea is to adapt models which were trained on existing, larger, noise-free corpora. In this study, we experimented with adapting Deep Neural Network-based acoustic models trained on noise-free speech data to perform speech recognition on utterances recorded by wireless sensors. In the end, we were able to achieve a 5% gain in terms of relative error reduction compared to training only on the sensor-recorded, restricted utterance subset.
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