TY - GEN A1 - Kraljevski, Ivan A1 - Duckhorn, Frank A1 - Ju, Yong Chul A1 - Tschöpe, Constanze A1 - Wolff, Matthias ED - Maglogiannis, Ilias ED - Iliadis, Lazaros S. ED - Pimenidis, Elias T1 - Acoustic Resonance Testing of Glass IV Bottles T2 - Artificial Intelligence Applications and Innovations : 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5–7, 2020, Proceedings, Part II N2 - 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. Y1 - 2020 SN - 978-3-030-49186-4 SN - 978-3-030-49185-7 U6 - https://doi.org/10.1007/978-3-030-49186-4_17 SN - 1868-4238 SN - 1868-422X VL - Cham SP - 195 EP - 206 PB - Springer International Publishing ER - TY - CHAP A1 - Tschöpe, Constanze A1 - Duckhorn, Frank A1 - Huber, Markus A1 - Meyer, Werner A1 - Wolff, Matthias ED - Karpov, Alexey ED - Jokisch, Oliver ED - Potapova, Rodmonga T1 - A Cognitive User Interface for a Multi-Modal Human-Machine Interaction T2 - Speech and computer : 20th International Conference, SPECOM 2018, Leipzig, Germany, September 18-22, 2018, proceedings Y1 - 2018 UR - https://link.springer.com/chapter/10.1007/978-3-319-99579-3_72 SN - 978-3-319-99578-6 U6 - https://doi.org/10.1007/978-3-319-99579-3 SP - 707 EP - 717 PB - Springer International Publishing CY - Cham ER - TY - GEN A1 - Duckhorn, Frank A1 - Huber, Markus A1 - Meyer, Werner A1 - Jokisch, Oliver A1 - Tschöpe, Constanze A1 - Wolff, Matthias ED - Lacerda, Francisco T1 - Towards an Autarkic Embedded Cognitive User Interface T2 - Proceedings Interspeech 2017, 20-24 August 2017, Stockholm N2 - ucuikt2015 Y1 - 2017 UR - http://www.isca-speech.org/archive/Interspeech_2017/ U6 - https://doi.org/10.21437/Interspeech.2017 SP - 3435 EP - 3436 PB - ISCA ER - TY - CHAP A1 - Tschöpe, Constanze A1 - Duckhorn, Frank A1 - Richter, Christian A1 - Blüthgen, Peter A1 - Wolff, Matthias T1 - Intelligent Signal Processing on a Miniaturized Hardware Module T2 - IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017 Y1 - 2017 UR - http://ieeexplore.ieee.org/document/8234023/ SN - 978-1-5090-1012-7 U6 - https://doi.org/10.1109/ICSENS.2017.8234023 N1 - IEEE Catalog Numer: CFP17SEN-ART PB - IEEE CY - Piscataway, NJ ER - TY - CHAP A1 - Tschöpe, Constanze A1 - Duckhorn, Frank A1 - Richter, Christian A1 - Blüthgen, Peter A1 - Wolff, Matthias T1 - An embedded system for acoustic pattern recognition T2 - IEEE SENSORS Proceedings, Glasgow, Scotland, UK, Oct. 29 - Nov. 1, 2017 Y1 - 2017 SN - 978-1-5090-1012-7 U6 - https://doi.org/10.1109/ICSENS.2017.8234380 N1 - IEEE Catalog Numer: CFP17SEN-ART PB - IEEE CY - Piscataway, NJ ER - TY - GEN A1 - Kraljevski, Ivan A1 - Rjelka, Marek A1 - Duckhorn, Frank A1 - Tschöpe, Constanze A1 - Wolff, Matthias ED - Hillmann, Stefan ED - Weiss, Benjamin ED - Michael, Thilo ED - Möller, Sebastian T1 - Cross-Lingual Acoustic Modeling in Upper Sorbian – Preliminary Study T2 - Elektronische Sprachsignalverarbeitung 2021 : Tagungsband der 32. Konferenz Berlin, 3.-5. März 2021 N2 - 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). Y1 - 2021 UR - https://publica.fraunhofer.de/dokumente/N-633297.html SN - 978-3-959082-27-3 SN - 0940-6832 SP - 43 EP - 50 PB - TUDpress CY - Dresden ER - TY - GEN A1 - Kraljevski, Ivan A1 - Duckhorn, Frank A1 - Tschöpe, Constanze A1 - Wolff, Matthias T1 - Machine Learning for Anomaly Assessment in Sensor Networks for NDT in Aerospace T2 - IEEE Sensors Journal N2 - 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. KW - Machine learning KW - Non-destructive testing KW - Ultrasonic transducers Y1 - 2021 UR - https://ieeexplore.ieee.org/document/9366491 U6 - https://doi.org/10.1109/JSEN.2021.3062941 SN - 1558-1748 VL - 21 IS - 9 SP - 11000 EP - 11008 ER - TY - GEN A1 - Kraljevski, Ivan A1 - Bissiri, Maria Paola A1 - Duckhorn, Frank A1 - Tschöpe, Constanze A1 - Wolff, Matthias T1 - Glottal Stops in Upper Sorbian: A Data-Driven Approach T2 - Proc. Interspeech 2021, 30 August – 3 September, 2021, Brno, Czechia N2 - 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. Y1 - 2021 UR - https://www.isca-speech.org/archive/interspeech_2021/kraljevski21_interspeech.html U6 - https://doi.org/10.21437/Interspeech.2021-1101 SP - 1001 EP - 1005 ER - TY - GEN A1 - Kraljevski, Ivan A1 - Duckhorn, Frank A1 - Tschöpe, Constanze A1 - Wolff, Matthias T1 - Convolutional Autoencoders for Health Indicators Extraction in Piezoelectric Sensors T2 - 2020 IEEE Sensors, 25-28 Oct. 2020, Rotterdam, Netherlands, N2 - 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. Y1 - 2020 UR - https://ieeexplore.ieee.org/document/9323023 SN - 978-1-7281-6801-2 U6 - https://doi.org/10.1109/SENSORS47125.2020.9323023 SP - 1 EP - 4 CY - Rotterdam, Netherlands ER - TY - GEN A1 - Maier, Isidor Konrad A1 - Kuhn, Johannes Ferdinand Joachim A1 - Duckhorn, Frank A1 - Kraljevski, Ivan A1 - Sobe, Daniel A1 - Wolff, Matthias A1 - Tschöpe, Constanze T1 - Word Class Based Language Modeling: A Case of Upper Sorbian T2 - 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 N2 - 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. KW - word classes, minimalist grammar, language modeling, speech recognition, Upper Sorbian Y1 - 2022 UR - http://www.lrec-conf.org/proceedings/lrec2022/workshops/EURALI/pdf/2022.eurali-1.5.pdf SN - 978-2-493814-07-4 SP - 28 EP - 35 PB - European Language Resources Association ER - TY - GEN A1 - Maier, Isidor Konrad A1 - Kuhn, Johannes A1 - Duckhorn, Frank A1 - Kraljevski, Ivan A1 - Sobe, Daniel A1 - Wolff, Matthias A1 - Tschöpe, Constanze T1 - Word Class Based Language Modeling: A Case of Upper Sorbian, LREC 2022, Marseille T2 - Language Resources and Evaluation Conference 2022 (LREC 2022), Marseille, 2022-06-13 Y1 - 2022 U6 - https://doi.org/10.5281/zenodo.7501145 ER -