@article{RykovaWalther2025, author = {Rykova, Eugenia and Walther, Mathias}, title = {Evaluation of German Automatic Speech Recognition solutions in the context of speech and language therapy support of people with aphasia}, series = {Loquens}, volume = {12}, journal = {Loquens}, publisher = {Centro de Ciencias Humanas y Sociales, Madrid}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-20615}, year = {2025}, abstract = {Those who suffer from aphasia benefit from digital speech and language therapy solutions, and automatic speech recognition (ASR) has been already used for giving feedback on the correctness of the answers in naming exercises. AphaDIGITAL application is to provide German-speaking users with detailed feedback on phonemic/phonetic and semantic errors, based on automatic speech and language processing. For this purpose, open-source ASR solutions for German were evaluated on different corpora of atypical speech, including two small datasets with aphasic speech samples. Character error rate, the number of precisely recognized items and empty outputs served as evaluation metrics. The four selected models are generally robust to the deteriorated condition of speech and audio quality and consistently outperform commercial models in atypical speech recognition. Applying error acceptance threshold, additional use of phonemic error rate, and other valuable insights for ASR implementation in aphaDIGITAL are discussed.}, language = {en} } @incollection{MoshnikovRykova2025, author = {Moshnikov, Ilia and Rykova, Eugenia}, title = {Collecting minority language data from Twitter (X): A case study of Karelian}, series = {Exploring digitally-mediated communication with corpora : Methods, analyses, and corpus construction}, booktitle = {Exploring digitally-mediated communication with corpora : Methods, analyses, and corpus construction}, editor = {Cotgrove, Louis and Herzberg, Laura and L{\"u}ngen, Harald}, publisher = {De Gruyter Brill}, isbn = {978-3-11-143401-8}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-20583}, publisher = {Technische Hochschule Wildau}, pages = {163 -- 186}, year = {2025}, abstract = {The visibility of an endangered language online plays a crucial role in language revitalisation. The internet offers a new domain for using minority languages, especially for speakers living outside the language communities. This article investigates Karelian language visibility on X, formerly known as Twitter, and describes the first corresponding data collection using language-related keywords and hashtags. In total, 2,625 entries written fully or partially in Livvi, South and Viena Karelian were scraped with Postman API. The visibility of Karelian on Twitter (X) has been increasing considerably in the past few years, with Livvi-Karelian being the most prominent dialect. Automatic language detection was tested on such data for Karelian for the first time, and allows the identification of Livvi-Karelian (or a mix of dialects that include Livvi-Karelian) with 99.7\% sensitivity, and South Karelian and Viena Karelian as Livvi-Karelian with 90\% and 73.8\% sensitivity, respectively. The entries were also analysed thematically, and 10 major topics were identified. Since the data was collected using keywords and hashtags related to the Karelian language itself, most of the entries are related to the language and vocabulary in sense of translation or language learning. Language status and policy is another important topic identified in the data. Although language-related topics are the most popular, there are a substantial number of entries on eight further topics. Excluding citations from religious texts and media headlines, 751 Twitter (X) entries could be used for linguistic and sociological research. Further data collection considerations are also discussed.}, language = {en} } @inproceedings{RykovaWalther2024, author = {Rykova, Eugenia and Walther, Mathias}, title = {Linguistic and extralinguistic factors in automatic speech recognition of German atypical speech}, series = {Proceedings of the 20th Conference on Natural Language Processing (KONVENS 2024)}, booktitle = {Proceedings of the 20th Conference on Natural Language Processing (KONVENS 2024)}, editor = {Luz de Araujo, Pedro Henrique and Baumann, Andreas and Gromann, Dagmar and Krenn, Brigitte and Roth, Benjamin and Wiegand, Michael}, publisher = {Association for Computational Linguistics}, address = {Wien}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19915}, pages = {358 -- 367}, year = {2024}, abstract = {Automatic speech recognition (ASR) has been already used in speech and language therapy, including diagnostic tasks and practice exercises for people with aphasia (PWA). The lack of relevant data makes it difficult to evaluate the algorithms' suitability for German-speaking PWA. For the current project, four open-source ASR models were selected based on their performance on other types of atypical speech, and the details of their evaluation are presented in this paper. The four selected models are generally robust to speakers' gender and age. The one-word recognition yields better results for words of moderate length. Speech rate should be neither too slow nor too quick for lower error rates both in words and phrases, and the latter should be also of moderate length.}, language = {en} } @inproceedings{RykovaWalther2024, author = {Rykova, Eugenia and Walther, Mathias}, title = {AphaDIGITAL - Digital Speech Therapy Solution for Aphasia Patients with Automatic Feedback Provided by a Virtual Assistant}, series = {Proceedings of the 57th Hawaii International Conference on System Sciences}, booktitle = {Proceedings of the 57th Hawaii International Conference on System Sciences}, editor = {of Hawai'i at Manoa, University}, isbn = {978-0-9981331-7-1}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19453}, pages = {3385 -- 3394}, year = {2024}, abstract = {This paper introduces aphaDIGITAL - a mobile application for speech and language therapy (SLT) support of German-speaking people with aphasia. The app includes automatic speech recognition and text analysis components, which allows giving detailed feedback to the users on different types of errors. Furthermore, the exercises are accompanied by an avatar-based SLT assistant, which is created for this specific purpose. The user performs the exercises, individually configured for her by the SLT practitioner, on a mobile device. Data collection and processing are carried out on the server.}, language = {en} } @inproceedings{MoshnikovRykova2023, author = {Moshnikov, Ilia and Rykova, Eugenia}, title = {Little Big Data: Karelian Twitter Corpus}, series = {Proceedings of the 10th International Conference on CMC and Social Media Corpora for the Humanities (CMC-Corpora 2023), 14-15 September 2023, University of Mannheim, Germany}, booktitle = {Proceedings of the 10th International Conference on CMC and Social Media Corpora for the Humanities (CMC-Corpora 2023), 14-15 September 2023, University of Mannheim, Germany}, editor = {Cotgrove, Louis and Herzberg, Laura and L{\"u}ngen, Harald and Pisetta, Ines}, publisher = {Leibniz-Institut f{\"u}r Deutsche Sprache (IDS)}, address = {Mannheim}, isbn = {978-3-937241-95-1}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-18018}, pages = {142 -- 147}, year = {2023}, abstract = {This paper investigates Karelian language visibility on Twitter and describes the first corresponding data collection using language-related keywords and hashtags. In total, 2626 entries written fully or partially in Livvi, South and Viena Karelian were scraped with Postman API. The visibility of Karelian on Twitter has been considerably increasing in the past few years, Livvi-Karelian being the most prominent dialect. The data were analysed linguistically (manually and with language detection software) and thematically. Although language-related topics are the most popular, there is a substantial number of entries in eight further topics. Applicability of the collected data for linguistic and sociological research, and further data collection considerations are discussed.}, language = {en} } @inproceedings{RykovaGolanovVogtetal.2023, author = {Rykova, Eugenia and Golanov, Juri and Vogt, Jonas and Rau, Daniel and Wieker, Horst}, title = {Traffic Data Evaluation for Automated Driving Handover Scenarios}, series = {Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems}, booktitle = {Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems}, editor = {Vinel, Alexey and Ploeg, Jeroen and Berns, Karsten and Gusikhin, Oleg}, publisher = {SciTePress}, address = {Set{\´u}bal, Portugal}, isbn = {978-989-758-652-1}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-17396}, pages = {125 -- 134}, year = {2023}, abstract = {At the current stage of automated vehicle development, the control handover from the system to a human driver (and back) is inevitable. It is essential to distinguish between situations in which the handover is possible and in which it could be dangerous and is therefore highly undesirable. We evaluated traffic situations based on two modalities: own vehicle state and traffic objects. To assess the former, supervised machine learning was applied, reaching an accuracy of 80.3\% and specificity of 77.8\% with Multilayer perceptron Classification. Traffic objects data were subject to different clustering techniques. The final grouping was done according to manually elaborated rules, resulting in a range of situation complexity scores. Improving the discriminative power of vehicle state classification, including driver's state and weather information, and predicting situation complexity are to be addressed in future research.}, language = {en} } @article{RykovaStiebenDostovalovaetal.2023, author = {Rykova, Eugenia and Stieben, Christine and Dostovalova, Olga and Wieker, Horst}, title = {Connected Driving in German-Speaking Social Media}, series = {Social Sciences}, volume = {12}, journal = {Social Sciences}, number = {1}, publisher = {MDPI}, issn = {2076-0760}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-16969}, year = {2023}, abstract = {Intelligent transportation systems (ITS) have been steadily becoming part of our reality. For their successful integration, studying and understanding public opinions and acceptance is important. Social media platforms offer an extensive opportunity for opinion mining. While there have been studies on people's attitudes towards automated driving, another important ITS concept—connected driving—has received little to no attention. In the current study, data on how connected driving is represented and perceived were collected from German(-speaking) Reddit and Twitter. In relevant Reddit entries, the necessity of communication between vehicles was discussed almost exclusively in the context of automated driving. On Twitter, mostly shared news and information on the topic are presented, while the number of personal opinions is low. The most concerning subtopic seems to be cybersecurity, which reflects a general trend of data protection issues discussed in society.}, language = {en} }