@inproceedings{TschoepeWolff, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Zur Formulierung von Hidden-Markov-Modellen als endliche Transduktoren}, series = {Elektronische Sprachsignalverarbeitung 2012, Tagungsband der 23. Konferenz, Cottbus, 29. - 31. August 2012}, booktitle = {Elektronische Sprachsignalverarbeitung 2012, Tagungsband der 23. Konferenz, Cottbus, 29. - 31. August 2012}, editor = {Wolff, Matthias}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-942710-81-7}, pages = {120 -- 128}, language = {de} } @inproceedings{TschoepeJoneitDuckhornetal., author = {Tsch{\"o}pe, Constanze and Joneit, Dieter and Duckhorn, Frank and Hoffmann, R{\"u}diger and Strecha, Guntram and Wolff, Matthias}, title = {Sprachsteuerung f{\"u}r Mess- und Pr{\"u}fger{\"a}te}, series = {DGZfP-Jahrestagung 2011 Zerst{\"o}rungsfreie Materialpr{\"u}fung, 30. Mai - 1. Juni 2011, Bremen, Berichtsband}, booktitle = {DGZfP-Jahrestagung 2011 Zerst{\"o}rungsfreie Materialpr{\"u}fung, 30. Mai - 1. Juni 2011, Bremen, Berichtsband}, publisher = {DGZfP}, address = {Berlin}, isbn = {978-3-940283-33-7}, pages = {6}, language = {de} } @inproceedings{WolffTschoepeRoemeretal., author = {Wolff, Matthias and Tsch{\"o}pe, Constanze and R{\"o}mer, Ronald and Wirsching, G{\"u}nther}, title = {Subsymbol-Symbol-Transduktoren}, series = {Elektronische Sprachsignalverarbeitung 2013, Tagungsband, Bielefeld, 2013}, booktitle = {Elektronische Sprachsignalverarbeitung 2013, Tagungsband, Bielefeld, 2013}, editor = {Wagner, Petra}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-94431-03-4}, pages = {197 -- 204}, language = {de} } @inproceedings{TschoepeWolffHoffmann, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias and Hoffmann, R{\"u}diger}, title = {Anwendungen der akustischen Mustererkennung}, series = {Elektronische Sprachsignalverarbeitung 2013, Tagungsband, Bielefeld, 2013}, booktitle = {Elektronische Sprachsignalverarbeitung 2013, Tagungsband, Bielefeld, 2013}, editor = {Wagner, Petra}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-94431-03-4}, pages = {205 -- 210}, language = {de} } @inproceedings{TschoepeWolffDuckhorn, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias and Duckhorn, Frank}, title = {Zustands{\"u}berwachung von Magnetventilen anhand der Schaltger{\"a}usche}, series = {ZfP in Forschung, Entwicklung und Anwendung, Potsdam, 26. - 28. Mai 2014, DGZfP-Jahrestagung 2014}, booktitle = {ZfP in Forschung, Entwicklung und Anwendung, Potsdam, 26. - 28. Mai 2014, DGZfP-Jahrestagung 2014}, publisher = {DGZfP}, address = {Berlin}, isbn = {978-3-940283-61-0}, language = {de} } @incollection{WolffTschoepeRoemer, author = {Wolff, Matthias and Tsch{\"o}pe, Constanze and R{\"o}mer, Ronald}, title = {Quo vadis, UASR?}, series = {Systemtheorie Signalverarbeitung Sprachtechnologie}, booktitle = {Systemtheorie Signalverarbeitung Sprachtechnologie}, editor = {Mehnert, Dieter and Kordon, Ulrich and Wolff, Matthias}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-944331-19-5}, pages = {276 -- 285}, language = {de} } @inproceedings{TschoepeWolff, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Processing and evaluation of gear data using statistical classifiers}, series = {Proceedings of the 6th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012), Vienna, Sep. 2012}, booktitle = {Proceedings of the 6th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2012), Vienna, Sep. 2012}, address = {Vienna}, isbn = {978-395-02481-9-7}, language = {en} } @misc{KraljevskiDuckhornJuetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Ju, Yong Chul and Tsch{\"o}pe, Constanze and Richter, Christian and Wolff, Matthias}, title = {Acoustic Resonance Recognition of Coins}, series = {2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 25-28 May 2020, Dubrovnik, Croatia}, journal = {2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 25-28 May 2020, Dubrovnik, Croatia}, publisher = {IEEE Xplore}, isbn = {978-1-7281-4460-3}, doi = {10.1109/I2MTC43012.2020.9129256}, pages = {6}, abstract = {In this study, we compare different machine learning approaches applied to acoustic resonance recognition of coins. Euro-cents and Euro-coins were classified by the sound emerging when throwing the coins onto a hard surface.The used dataset is a representative example of a small data which was collected in carefully prepared experiments.Due to the small number of coin specimens and the count of the collected observations, it was interesting to see whether deep learning methods can achieve similarly or maybe even better classification performances compared with more traditional methods.The results of the multi-class prediction of coin denominations are presented and compared in terms of balanced accuracy and Matthews Correlation Coefficient metrics. The feature analysis methods combined with the employed classifiers achieved acceptable results, despite the relatively small dataset.}, language = {en} } @misc{KraljevskiDuckhornJuetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Ju, Yong Chul and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Acoustic Resonance Testing of Glass IV Bottles}, series = {Artificial Intelligence Applications and Innovations : 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5-7, 2020, Proceedings, Part II}, volume = {Cham}, journal = {Artificial Intelligence Applications and Innovations : 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5-7, 2020, Proceedings, Part II}, editor = {Maglogiannis, Ilias and Iliadis, Lazaros S. and Pimenidis, Elias}, publisher = {Springer International Publishing}, isbn = {978-3-030-49186-4}, issn = {1868-4238}, doi = {10.1007/978-3-030-49186-4_17}, pages = {195 -- 206}, abstract = {In this paper, acoustic resonance testing on glass intravenous (IV) bottles is presented. Different machine learning methods were applied to distinguish acoustic observations of bottles with defects from the intact ones. Due to the very limited amount of available specimens, the question arises whether the deep learning methods can achieve similar or even better detection performance compared with traditional methods.}, language = {en} } @inproceedings{TschoepeDuckhornHuberetal., author = {Tsch{\"o}pe, Constanze and Duckhorn, Frank and Huber, Markus and Meyer, Werner and Wolff, Matthias}, title = {A Cognitive User Interface for a Multi-Modal Human-Machine Interaction}, series = {Speech and computer : 20th International Conference, SPECOM 2018, Leipzig, Germany, September 18-22, 2018, proceedings}, booktitle = {Speech and computer : 20th International Conference, SPECOM 2018, Leipzig, Germany, September 18-22, 2018, proceedings}, editor = {Karpov, Alexey and Jokisch, Oliver and Potapova, Rodmonga}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-319-99578-6}, doi = {10.1007/978-3-319-99579-3}, pages = {707 -- 717}, language = {en} } @misc{DuckhornHuberMeyeretal., author = {Duckhorn, Frank and Huber, Markus and Meyer, Werner and Jokisch, Oliver and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Towards an Autarkic Embedded Cognitive User Interface}, series = {Proceedings Interspeech 2017, 20-24 August 2017, Stockholm}, journal = {Proceedings Interspeech 2017, 20-24 August 2017, Stockholm}, editor = {Lacerda, Francisco}, publisher = {ISCA}, doi = {10.21437/Interspeech.2017}, pages = {3435 -- 3436}, abstract = {ucuikt2015}, language = {en} } @inproceedings{TschoepeDuckhornRichteretal., author = {Tsch{\"o}pe, Constanze and Duckhorn, Frank and Richter, Christian and Bl{\"u}thgen, Peter and Wolff, Matthias}, title = {Intelligent Signal Processing on a Miniaturized Hardware Module}, series = {IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017}, booktitle = {IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-5090-1012-7}, doi = {10.1109/ICSENS.2017.8234023}, language = {en} } @inproceedings{TschoepeWolff, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Mustererkennung in der technischen Diagnose}, series = {Tagungsband der 12. Tagung Technische Diagnostik 2016, 20.-21.10.2016, Hochschule Merseburg}, booktitle = {Tagungsband der 12. Tagung Technische Diagnostik 2016, 20.-21.10.2016, Hochschule Merseburg}, publisher = {Hochschule Merseburg}, address = {Merseburg}, isbn = {978-3-942703-64-2}, url = {http://nbn-resolving.de/urn:nbn:de:gbv:3:2-68977}, pages = {124 -- 125}, language = {de} } @inproceedings{TschoepeDuckhornRichteretal., author = {Tsch{\"o}pe, Constanze and Duckhorn, Frank and Richter, Christian and Bl{\"u}thgen, Peter and Wolff, Matthias}, title = {An embedded system for acoustic pattern recognition}, series = {IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017}, booktitle = {IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017}, publisher = {IEEE}, address = {Piscataway, NJ}, isbn = {978-1-5090-1012-7}, doi = {10.1109/ICSENS.2017.8234380}, pages = {3}, language = {en} } @inproceedings{TschoepeWolffSaeltzer, author = {Tsch{\"o}pe, Constanze and Wolff, Matthias and Saeltzer, G.}, title = {Estimating blood sugar from voice samples : a preliminary study}, series = {2015 International Conference on Computational Science and Computational Intelligence (CSCI 2015), Las Vegas, December 7-9, 2015}, booktitle = {2015 International Conference on Computational Science and Computational Intelligence (CSCI 2015), Las Vegas, December 7-9, 2015}, publisher = {IEEE}, doi = {10.1109/CSCI.2015.184}, pages = {804 -- 805}, language = {en} } @misc{TschoepeMuehleJuetal., author = {Tsch{\"o}pe, Constanze and M{\"u}hle, Maximilian and Ju, Yong Chul and Kraljevski, Ivan and Wolff, Matthias}, title = {K{\"u}nstliche Intelligenz in der ZfP - Welchen Beitrag kann KI in der ZfP leisten?}, series = {DGZfP-Jahrestagung 2021, 10.-11. Mai}, journal = {DGZfP-Jahrestagung 2021, 10.-11. Mai}, isbn = {978-3-947971-18-3}, pages = {17}, abstract = {K{\"u}nstliche Intelligenz zieht derzeit in alle Bereiche der Gesellschaft und des Lebens ein. Aber welchen Stellenwert hat sie momentan auf dem Gebiet der zerst{\"o}rungsfreien Pr{\"u}fung? Was kann KI leisten? Welche Herausforderungen m{\"u}ssen erfolgreich bew{\"a}ltigt werden? Gibt es das eine KI-Verfahren, welches prinzipiell f{\"u}r ZfP geeignet ist? Bei der Bauteil- und Materialpr{\"u}fung w{\"a}hrend und unmittelbar nach der Herstellung, der {\"U}berwachung von Verschleißteilen in Maschinen und Anlagen oder der Schadensdetektion an Bauteilen und Komponenten liefern ZfP-Verfahren Daten, die bewertet werden m{\"u}ssen. Obwohl inzwischen sehr leistungsf{\"a}hige Toolkits verf{\"u}gbar sind, erfordert der optimale Einsatz der KI f{\"u}r ein ZfP-Verfahren oftmals mehr. Die meisten Kunden m{\"o}chten nicht nur eine L{\"o}sung ihres Problems; sie wollen verstehen, warum die KI so und nicht anders entschieden hat, warum der Klassifikator das Bauteil einer bestimmten Klasse (z. B. gut/schlecht oder neuwertig/verschlissen/defekt) zugewiesen hat. Abh{\"a}ngig von der Klassifikationsaufgabe sowie der Art und der Anzahl der vorliegenden Daten kann ein geeignetes Verfahren bestimmt werden. Mit Methoden des maschinellen Lernens werden Modelle gebildet, welche die Basis f{\"u}r die KI-Verfahren zur Klassifikation bilden. Der Beitrag liefert einen {\"U}berblick {\"u}ber KI-Verfahren und deren Anwendungen in der zerst{\"o}rungsfreien Pr{\"u}fung. Zahlreiche Beispiele und Ergebnisse werden vorgestellt, um die Mannigfaltigkeit des Einsatzes in der ZfP und der bestehenden M{\"o}glichkeiten zu demonstrieren.}, language = {de} } @misc{KraljevskiRjelkaDuckhornetal., author = {Kraljevski, Ivan and Rjelka, Marek and Duckhorn, Frank and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Cross-Lingual Acoustic Modeling in Upper Sorbian - Preliminary Study}, series = {Elektronische Sprachsignalverarbeitung 2021 : Tagungsband der 32. Konferenz Berlin, 3.-5. M{\"a}rz 2021}, journal = {Elektronische Sprachsignalverarbeitung 2021 : Tagungsband der 32. Konferenz Berlin, 3.-5. M{\"a}rz 2021}, editor = {Hillmann, Stefan and Weiss, Benjamin and Michael, Thilo and M{\"o}ller, Sebastian}, publisher = {TUDpress}, address = {Dresden}, isbn = {978-3-959082-27-3}, issn = {0940-6832}, pages = {43 -- 50}, abstract = {In this paper, we present a preliminary study for acoustic modeling in Upper Sorbian, where a model of German was used in cross-lingual transfer learning. At first, we define the grapheme and phoneme inventories and map the target phonemes from the most similar German source equivalents. Phonetically balanced sentences for the recording prompts were selected from a combination of general and domain-specific textual data. The speech corpora with a total duration of around 11 hours was collected in controlled recording sessions involving an equal number of females, males, and children. The baseline acoustic model was employed to force-align the speech corpora given the knowledge-based phoneme mappings. How well the mappings were, was evaluated by the phoneme confusions in free-phoneme recognition. The new derived data-driven model with a reduced phoneme set was included in the adaptation and evaluation along with the baseline acoustic model. The model adaptation performance was cross-validated with the "Leave One Group Out" strategy. We observed major improvements in phoneme error rates after adaptation for the knowledge-based and data-driven phoneme mappings. The study confirmed the feasibility of transfer learning for acoustic model adaptation in the case of Upper Sorbian, at the same time demonstrating practical usability with a small vocabulary speech recognition application (Smart Lamp).}, language = {de} } @misc{KraljevskiDuckhornTschoepeetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Machine Learning for Anomaly Assessment in Sensor Networks for NDT in Aerospace}, series = {IEEE Sensors Journal}, volume = {21}, journal = {IEEE Sensors Journal}, number = {9}, issn = {1558-1748}, doi = {10.1109/JSEN.2021.3062941}, pages = {11000 -- 11008}, abstract = {We investigated and compared various algorithms in machine learning for anomaly assessment with different feature analyses on ultrasonic signals recorded by sensor networks. The following methods were used and compared in anomaly detection modeling: hidden Markov models (HMM), support vector machines (SVM), isolation forest (IF), and reconstruction autoencoders (AEC). They were trained exclusively on sensor signals of the intact state of structures commonly used in various industries, like aerospace and automotive. The signals obtained on artificially introduced damage states were used for performance evaluation. Anomaly assessment was evaluated and compared using various classifiers and feature analysis methods. We introduced novel methodologies for two processes. The first was the dataset preparation with anomalies. The second was the detection and damage severity assessment utilizing the intact object state exclusively. The experiments proved that robust anomaly detection is practically feasible. We were able to train accurate classifiers which had a considerable safety margin. Precise quantitative analysis of damage severity will also be possible when calibration data become available during exploitation or by using expert knowledge.}, language = {en} } @misc{KraljevskiBissiriDuckhornetal., author = {Kraljevski, Ivan and Bissiri, Maria Paola and Duckhorn, Frank and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Glottal Stops in Upper Sorbian: A Data-Driven Approach}, series = {Proc. Interspeech 2021, 30 August - 3 September, 2021, Brno, Czechia}, journal = {Proc. Interspeech 2021, 30 August - 3 September, 2021, Brno, Czechia}, doi = {10.21437/Interspeech.2021-1101}, pages = {1001 -- 1005}, abstract = {We present a data-driven approach for the quantitative analysis of glottal stops before word-initial vowels in Upper Sorbian, a West Slavic minority language spoken in Germany. Glottal stops are word-boundary markers and their detection can improve the performance of automatic speech recognition and speech synthesis systems. We employed cross-language transfer using an acoustic model in German to develop a forced-alignment method for the phonetic segmentation of a read-speech corpus in Upper Sorbian. The missing phonemic units were created by combining the existing phoneme models. In the forced-alignment procedure, the glottal stops were considered optional in front of word-initial vowels. To investigate the influence of speaker type (males, females, and children) and vowel on the occurrence of glottal stops, binomial regression analysis with a generalized linear mixed model was performed. Results show that children glottalize word-initial vowels more frequently than adults, and that glottal stop occurrences are influenced by vowel quality.}, language = {en} } @misc{KraljevskiDuckhornBarthetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Barth, Martin and Tsch{\"o}pe, Constanze and Schubert, Frank and Wolff, Matthias}, title = {Autoencoder-based Ultrasonic NDT of Adhesive Bonds}, series = {IEEE SENSORS 2021, Conference Proceedings, Oct 31- Nov 4, Sydney, Australia}, journal = {IEEE SENSORS 2021, Conference Proceedings, Oct 31- Nov 4, Sydney, Australia}, publisher = {IEEE}, isbn = {978-1-7281-9501-8}, doi = {10.1109/SENSORS47087.2021.9639864}, pages = {4}, abstract = {We present an approach for ultrasonic non-destructive testing of adhesive bonding employing unsupervised machine learning with autoencoders.The models are trained exclusively on the features derived from pulse-echo ultrasonic signals on a specimen with good adhesive bonding and tested on another specimen with artificially added defects.The resulting pseudo-probabilities indicating anomalies are visualized and presented along to the C-scan of the same specimen. As a result, we achieved improved representation of the defects, allowing their automatic and reliable detection.}, language = {en} } @misc{KraljevskiDuckhornTschoepeetal., author = {Kraljevski, Ivan and Duckhorn, Frank and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Convolutional Autoencoders for Health Indicators Extraction in Piezoelectric Sensors}, series = {2020 IEEE Sensors, 25-28 Oct. 2020, Rotterdam, Netherlands,}, journal = {2020 IEEE Sensors, 25-28 Oct. 2020, Rotterdam, Netherlands,}, address = {Rotterdam, Netherlands}, isbn = {978-1-7281-6801-2}, doi = {10.1109/SENSORS47125.2020.9323023}, pages = {1 -- 4}, abstract = {We present a method for extracting health indicators from piezoelectric sensors applied in the case of microfluidic valves. Convolutional autoencoders were used to train a model on the normal operating conditions and tested on signals of different valves. The results of the model performance evaluation, as well as, the qualitative presentation of the indicator plots for each tested component, showed that the used approach is capable of detecting features that correspond to increasing component degradation. The extracted health indicators are the prerequisite and input for reliable remaining useful life prediction.}, language = {en} } @misc{KlimczakKuscheTschoepeetal., author = {Klimczak, Peter and Kusche, Isabel and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Menschliche und maschinelle Entscheidungsrationalit{\"a}t - Zur Kontrolle und Akzeptanz K{\"u}nstlicher Intelligenz}, series = {Zeitschrift f{\"u}r Medienwissenschaft 21 - K{\"u}nstliche Intelligenz}, journal = {Zeitschrift f{\"u}r Medienwissenschaft 21 - K{\"u}nstliche Intelligenz}, number = {2}, isbn = {978-3-8376-4468-5}, issn = {1869-1722}, doi = {/10.25969/mediarep/12631}, pages = {39 -- 45}, language = {de} } @incollection{KraljevskiTschoepeWolff, author = {Kraljevski, Ivan and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {Limits and Prospects of Big Data and Small Data Approaches in AI Applications}, series = {AI - Limits and Prospects of Artificial Intelligence}, booktitle = {AI - Limits and Prospects of Artificial Intelligence}, editor = {Klimczak, Peter and Petersen, Christer}, publisher = {transcript Verlag}, address = {Bielefeld}, isbn = {978-3-8376-5732-6}, doi = {10.14361/9783839457320-006}, pages = {115 -- 142}, abstract = {The renaissance of artificial intelligence (AI) in the last decade can be credited to several factors, but chief among these is the ever-increasing availability and miniaturization of computational resources. This process has contributed to the rise of ubiquitous computing via popularizing smart devices and the Internet of Things in everyday life. In turn, this has resulted in the generation of increasingly enormous amounts of data. The tech giants are harvesting and storing data on their clients' behavior and, at the same time, introducing concerns about data privacy and protection. Suddenly, such an abundance of data and computing power, which was unimaginable a few decades ago, has caused a revival of old and the invention of new machine learning paradigms, like Deep Learning. Artificial intelligence has undergone a technological breakthrough in various fields, achieving better than human performance in many areas (such as vision, board games etc.). More complex tasks require more sophisticated algorithms that need more and more data. It has often been said that data is becoming a resource that is "more valuable than oil"; however, not all data is equally available and obtainable. Big data can be described by using the "four Vs"; data with immense velocity, volume, variety, and low veracity. In contrast, small data do not possess any of those qualities; they are limited in size and nature and are observed or produced in a controlled manner. Big data, along with powerful computing and storage resources, allow "black box" AI algorithms for various problems previously deemed unsolvable. One could create AI applications even without the underlying expert knowledge, assuming there are enough data and the right tools available (e.g. end-to-end speech recognition and generation, image and object recognition). There are numerous fields in science, industry and everyday life where AI has vast potential. However, due to the lack of big data, application is not straightforward or even possible. A good example is AI in medicine, where an AI system is intended to assist physicians in diagnosing and treating rare or previously never observed conditions, and there is no or an insufficient amount of data for reliable AI deployment. Both big and small data concepts have limitations and prospects for different fields of application. This paper will try to identify and present them by giving real-world examples in various AI fields.}, language = {en} } @misc{UhligAlkhasliSchubertetal., author = {Uhlig, Sebastian and Alkhasli, Ilkin and Schubert, Frank and Tsch{\"o}pe, Constanze and Wolff, Matthias}, title = {A Review of Synthetic and Augmented Training Data for Machine Learning in Ultrasonic Non-Destructive Evaluation}, series = {Ultrasonics}, journal = {Ultrasonics}, number = {134}, issn = {1874-9968}, doi = {10.1016/j.ultras.2023.107041}, abstract = {Ultrasonic Testing (UT) has seen increasing application of machine learning (ML) in recent years, promoting higher-level automation and decision-making in flaw detection and classification. Building a generalized training dataset to apply ML in non-destructive evaluation (NDE), and thus UT, is exceptionally difficult since data on pristine and representative flawed specimens are needed. Yet, in most UT test cases flawed specimen data is inherently rare making data coverage the leading problem when applying ML. Common data augmentation (DA) strategies offer limited solutions as they don't increase the dataset variance, which can lead to overfitting of the training data. The virtual defect method and the recent application of generative adversarial neural networks (GANs) in UT are sophisticated DA methods targeting to solve this problem. On the other hand, well-established research in modeling ultrasonic wave propagations allows for the generation of synthetic UT training data. In this context, we present a first thematic review to summarize the progress of the last decades on synthetic and augmented UT training data in NDE. Additionally, an overview of methods for synthetic UT data generation and augmentation is presented. Among numerical methods such as finite element, finite difference, and elastodynamic finite integration methods, semi-analytical methods such as general point source synthesis, superposition of Gaussian beams, and the pencil method as well as other UT modeling software are presented and discussed. Likewise, existing DA methods for one- and multidimensional UT data, feature space augmentation, and GANs for augmentation are presented and discussed. The paper closes with an in-detail discussion of the advantages and limitations of existing methods for both synthetic UT training data generation and DA of UT data to aid the decision-making of the reader for the application to specific test cases.}, language = {en} } @misc{MaierKuhnDuckhornetal., author = {Maier, Isidor Konrad and Kuhn, Johannes Ferdinand Joachim and Duckhorn, Frank and Kraljevski, Ivan and Sobe, Daniel and Wolff, Matthias and Tsch{\"o}pe, Constanze}, title = {Word Class Based Language Modeling: A Case of Upper Sorbian}, series = {Proceedings of The Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia within the 13th Language Resources and Evaluation Conference, Marseille, France}, journal = {Proceedings of The Workshop on Resources and Technologies for Indigenous, Endangered and Lesser-resourced Languages in Eurasia within the 13th Language Resources and Evaluation Conference, Marseille, France}, publisher = {European Language Resources Association}, isbn = {978-2-493814-07-4}, pages = {28 -- 35}, abstract = {In this paper we show how word class based language modeling can support the integration of a small language in modern applications of speech technology. The methods described in this paper can be applied for any language. We demonstrate the methods on Upper Sorbian. The word classes model the semantic expressions of numerals, date and time of day. The implementation of the created grammars was realized in the form of finite-state-transducers (FSTs) and minimalists grammars (MGs). We practically demonstrate the usage of the FSTs in a simple smart-home speech application, that is able to set wake-up alarms and appointments expressed in a variety of spontaneous and natural sentences. While the created MGs are not integrated in an application for practical use yet, they provide evidence that MGs could potentially work more efficient than FSTs in built-on applications. In particular, MGs can work with a significantly smaller lexicon size, since their more complex structure lets them generate more expressions with less items, while still avoiding wrong expressions.}, language = {en} }