TY - GEN A1 - Werner, Steffen A1 - Eichner, Matthias A1 - Wolff, Matthias A1 - Hoffmann, Rüdiger T1 - Towards spontaneous speech synthesis - Utilizing language model information in TTS T2 - IEEE transactions on speech and audio processing Y1 - 2004 U6 - https://doi.org/10.1109/TSA.2004.828635 SN - 1063-6676 VL - 12 IS - 4 SP - 436 EP - 445 ER - TY - GEN A1 - Kordon, Ulrich A1 - Wolff, Matthias A1 - Kürbis, Steffen T1 - Laufzeitmessung von gestörten akustischen Signalen mit Verfahren der Mustererkennung Y1 - 2010 ER - TY - GEN A1 - Maier, Isidor Konrad A1 - Kuhn, Johannes A1 - Beisegel, Jesse A1 - Huber-Liebl, Markus A1 - Wolff, Matthias T1 - Minimalist Grammar: Construction without Overgeneration T2 - arXiv N2 - In this paper we give instructions on how to write a minimalist grammar (MG). In order to present the instructions as an algorithm, we use a variant of context free grammars (CFG) as an input format. We can exclude overgeneration, if the CFG has no recursion, i.e. no non-terminal can (indirectly) derive to a right-hand side containing itself. The constructed MGs utilize licensors/-ees as a special way of exception handling. A CFG format for a derivation A_eats_B↦∗peter_eats_apples, where A and B generate noun phrases, normally leads to overgeneration, e.\,g., i_eats_apples. In order to avoid overgeneration, a CFG would need many non-terminal symbols and rules, that mainly produce the same word, just to handle exceptions. In our MGs however, we can summarize CFG rules that produce the same word in one item and handle exceptions by a proper distribution of licensees/-ors. The difficulty with this technique is that in most generations the majority of licensees/-ors is not needed, but still has to be triggered somehow. We solve this problem with ϵ-items called \emph{adapters}. Y1 - 2023 UR - https://arxiv.org/abs/2311.01820 U6 - https://doi.org/10.48550/arXiv.2311.01820 ER - TY - GEN A1 - Pusch, T. A1 - Cherif, Chokri A1 - Farooq, Aamir A1 - Wittenberg, Sören A1 - Wolff, Matthias A1 - Hoffmann, Rüdiger A1 - Tschöpe, Constanze T1 - Fehlerfrüherkennung an Textilmaschinen mit Hilfe der Körperschallanalyse T2 - Melliand Textilberichte Y1 - 2009 SN - 0341-0781 VL - 90 IS - 3 SP - 113 EP - 115 ER - TY - GEN A1 - Kraljevski, Ivan A1 - Ju, Yong Chul A1 - Ivanov, Dmitrij A1 - Tschöpe, Constanze A1 - Wolff, Matthias T1 - How to Do Machine Learning with Small Data? - A Review from an Industrial Perspective T2 - arXiv N2 - Artificial intelligence experienced a technological breakthrough in science, industry, and everyday life in the recent few decades. The advancements can be credited to the ever-increasing availability and miniaturization of computational resources that resulted in exponential data growth. However, because of the insufficient amount of data in some cases, employing machine learning in solving complex tasks is not straightforward or even possible. As a result, machine learning with small data experiences rising importance in data science and application in several fields. The authors focus on interpreting the general term of "small data" and their engineering and industrial application role. They give a brief overview of the most important industrial applications of machine learning and small data. Small data is defined in terms of various characteristics compared to big data, and a machine learning formalism was introduced. Five critical challenges of machine learning with small data in industrial applications are presented: unlabeled data, imbalanced data, missing data, insufficient data, and rare events. Based on those definitions, an overview of the considerations in domain representation and data acquisition is given along with a taxonomy of machine learning approaches in the context of small data. Y1 - 2023 UR - https://arxiv.org/abs/2311.07126 ER -