TY - CHAP A1 - Bayerl, Sebastian P. A1 - Hönig, Florian A1 - Reister, Joelle A1 - Riedhammer, Korbinian T1 - Towards automated assessment of stuttering and stuttering therapy N2 - Stuttering is a complex speech disorder that can be identified by repetitions, prolongations of sounds, syllables or words and blocks while speaking. Severity assessment is usually done by a speech therapist. While attempts at automated assessment were made, it is rarely used in therapy. Common methods for the assessment of stuttering severity include percent stuttered syllables (% SS), the average of the three longest stuttering symptoms during a speech task or the recently introduced Speech Efficiency Score (SES). This paper introduces the Speech Control Index (SCI), a new method to evaluate the severity of stuttering. Unlike SES, it can also be used to assess therapy success for fluency shaping. We evaluate both SES and SCI on a new comprehensively labeled dataset containing stuttered German speech of clients prior to, during and after undergoing stuttering therapy. Phone alignments of an automatic speech recognition system are statistically evaluated in relation to their relative position to labeled stuttering events. The results indicate that phone length distributions differ in respect to their position in and around labeled stuttering events. KW - speech and voice disorders · pathological speech · language Y1 - 2020 U6 - https://doi.org/10.48550/arXiv.2006.09222 ER - TY - CHAP A1 - Bayerl, Sebastian P. A1 - Gudenberg, AlexanderWolffvon A1 - Hönig, Florian A1 - Nöth, Elmar A1 - Riedhammer, Korbinian T1 - KSoF : The Kassel State of Fluency Dataset BT - A Therapy Centered Dataset of Stuttering N2 - Stuttering is a complex speech disorder that negatively affects an individual's ability to communicate effectively. Persons who stutter (PWS) often suffer considerably under the condition and seek help through therapy. Fluency shaping is a therapy approach where PWSs learn to modify their speech to help them to overcome their stutter. Mastering such speech techniques takes time and practice, even after therapy. Shortly after therapy, success is evaluated highly, but relapse rates are high. To be able to monitor speech behavior over a long time, the ability to detect stuttering events and modifications in speech could help PWSs and speech pathologists to track the level of fluency. Monitoring could create the ability to intervene early by detecting lapses in fluency. To the best of our knowledge, no public dataset is available that contains speech from people who underwent stuttering therapy that changed the style of speaking. This work introduces the Kassel State of Fluency (KSoF), a therapy-based dataset containing over 5500 clips of PWSs. The clips were labeled with six stuttering-related event types: blocks, prolongations, sound repetitions, word repetitions, interjections, and - specific to therapy - speech modifications. The audio was recorded during therapy sessions at the Institut der Kasseler Stottertherapie. The data will be made available for research purposes upon request. KW - stuttering, pathological speech, corpus, speech resource Y1 - 2022 U6 - https://doi.org/10.48550/arXiv.2203.05383 ER - TY - CHAP A1 - Braun, Franziska A1 - Bayerl, Sebastian P. A1 - Pérez-Toro, Paula A. A1 - Hönig, Florian A1 - Lehfeld, Hartmut A1 - Hillemacher, Thomas A1 - Nöth, Elmar A1 - Bocklet, Tobias A1 - Riedhammer, Korbinian T1 - Classifying Dementia in the Presence of Depression BT - A Cross-Corpus Study N2 - Automated dementia screening enables early detection and intervention, reducing costs to healthcare systems and increasing quality of life for those affected. Depression has shared symptoms with dementia, adding complexity to diagnoses. The research focus so far has been on binary classification of dementia (DEM) and healthy controls (HC) using speech from picture description tests from a single dataset. In this work, we apply established baseline systems to discriminate cognitive impairment in speech from the semantic Verbal Fluency Test and the Boston Naming Test using text, audio and emotion embeddings in a 3-class classification problem (HC vs. MCI vs. DEM). We perform cross-corpus and mixed-corpus experiments on two independently recorded German datasets to investigate generalization to larger populations and different recording conditions. In a detailed error analysis, we look at depression as a secondary diagnosis to understand what our classifiers actually learn. Y1 - 2023 U6 - https://doi.org/10.48550/arXiv.2308.08306 ER -