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Book ; Online: Enhancing the Levelized Cost of Hydrogen with the Usage of the Byproduct Oxygen in a Wastewater Treatment Plant

Hönig, Franziska / Rupakula, Ganesh Deepak / Duque-Gonzalez, Diana / Ebert, Matthias / Blum, Ulrich

2023

Abstract: In order to harmonize the supply and demand of green energy, new future-proof technologies are needed. Here, hydrogen plays a key role. Within the current framework conditions, the production of green hydrogen is not yet economically viable. The use of ... ...

Abstract	In order to harmonize the supply and demand of green energy, new future-proof technologies are needed. Here, hydrogen plays a key role. Within the current framework conditions, the production of green hydrogen is not yet economically viable. The use of the oxygen produced and the possible increase in efficiency associated with it mostly remain unconsidered. The aim is to demonstrate that the economic efficiency of a power-to-gas (PtG) project can be increased by using the byproduct oxygen. In this research project, a water electrolyzer connected to grid is powered to supply hydrogen to a hydrogen refueling station. By utilizing the byproduct oxygen from water electrolysis for a wastewater treatment plant (WWTP), it is shown that the net present value (NPV) of the project can be improved by up to 13% compared to the initial scenario. If a photovoltaic (PV) system is used in addition to grid electricity for higher green hydrogen production, the NPV can be further improved by up to 58%. The levelized cost of hydrogen (LCOH) is calculated for different scenarios with and without oxygen configuration. A sensitivity analysis is then performed to find important parameters. 16 12
Keywords	alkaline water electrolysis ; byproduct oxygen ; hydrogen ; levelized cost of hydrogen ; power-to-gas ; wastewater treatment plant
Subject code	660
Language	English
Publishing country	de
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Dysfluencies Seldom Come Alone -- Detection as a Multi-Label Problem

Bayerl, Sebastian P. / Wagner, Dominik / Hönig, Florian / Bocklet, Tobias / Nöth, Elmar / Riedhammer, Korbinian

2022

Abstract: Specially adapted speech recognition models are necessary to handle stuttered speech. For these to be used in a targeted manner, stuttered speech must be reliably detected. Recent works have treated stuttering as a multi-class classification problem or ... ...

Abstract	Specially adapted speech recognition models are necessary to handle stuttered speech. For these to be used in a targeted manner, stuttered speech must be reliably detected. Recent works have treated stuttering as a multi-class classification problem or viewed detecting each dysfluency type as an isolated task; that does not capture the nature of stuttering, where one dysfluency seldom comes alone, i.e., co-occurs with others. This work explores an approach based on a modified wav2vec 2.0 system for end-to-end stuttering detection and classification as a multi-label problem. The method is evaluated on combinations of three datasets containing English and German stuttered speech, yielding state-of-the-art results for stuttering detection on the SEP-28k-Extended dataset. Experimental results provide evidence for the transferability of features and the generalizability of the method across datasets and languages. Comment: Submitted to ICASSP 2023
Keywords	Electrical Engineering and Systems Science - Audio and Speech Processing ; Computer Science - Sound
Subject code	006
Publishing date	2022-10-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: KSoF

Bayerl, Sebastian P. / von Gudenberg, Alexander Wolff / Hönig, Florian / Nöth, Elmar / Riedhammer, Korbinian

The Kassel State of Fluency Dataset -- A Therapy Centered Dataset of Stuttering

2022

Abstract: 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 ... ...

Abstract	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. Comment: Accepted at LREC 2022 Conference on Language Resources and Evaluation
Keywords	Electrical Engineering and Systems Science - Audio and Speech Processing ; Computer Science - Computation and Language
Subject code	400
Publishing date	2022-03-10
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: A Stutter Seldom Comes Alone -- Cross-Corpus Stuttering Detection as a Multi-label Problem

Bayerl, Sebastian P. / Wagner, Dominik / Baumann, Ilja / Hönig, Florian / Bocklet, Tobias / Nöth, Elmar / Riedhammer, Korbinian

2023

Abstract: Most stuttering detection and classification research has viewed stuttering as a multi-class classification problem or a binary detection task for each dysfluency type; however, this does not match the nature of stuttering, in which one dysfluency seldom ...

Abstract	Most stuttering detection and classification research has viewed stuttering as a multi-class classification problem or a binary detection task for each dysfluency type; however, this does not match the nature of stuttering, in which one dysfluency seldom comes alone but rather co-occurs with others. This paper explores multi-language and cross-corpus end-to-end stuttering detection as a multi-label problem using a modified wav2vec 2.0 system with an attention-based classification head and multi-task learning. We evaluate the method using combinations of three datasets containing English and German stuttered speech, one containing speech modified by fluency shaping. The experimental results and an error analysis show that multi-label stuttering detection systems trained on cross-corpus and multi-language data achieve competitive results but performance on samples with multiple labels stays below over-all detection results. Comment: Accepted for presentation at Interspeech 2023. arXiv admin note: substantial text overlap with arXiv:2210.15982
Keywords	Electrical Engineering and Systems Science - Audio and Speech Processing ; Computer Science - Computation and Language ; Computer Science - Sound
Subject code	006
Publishing date	2023-05-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Towards Automated Assessment of Stuttering and Stuttering Therapy

Bayerl, Sebastian P. / Hönig, Florian / Reister, Joelle / Riedhammer, Korbinian

2020

Abstract: 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 ... ...

Abstract	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 with respect to their position in and around labeled stuttering events Comment: 10 pages, 3 figures, 1 table Accepted at TSD 2020, 23rd International Conference on Text, Speech and Dialogue
Keywords	Quantitative Biology - Quantitative Methods ; Computer Science - Computation and Language ; Computer Science - Machine Learning ; Computer Science - Sound ; Electrical Engineering and Systems Science - Audio and Speech Processing
Subject code	410
Publishing date	2020-06-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models.

Martindale, Christine F / Hoenig, Florian / Strohrmann, Christina / Eskofier, Bjoern M

Sensors (Basel, Switzerland)

2017 Volume 17, Issue 10

Abstract: Cyclic signals are an intrinsic part of daily life, such as human motion and heart activity. The detailed analysis of them is important for clinical applications such as pathological gait analysis and for sports applications such as performance analysis. ...

Abstract	Cyclic signals are an intrinsic part of daily life, such as human motion and heart activity. The detailed analysis of them is important for clinical applications such as pathological gait analysis and for sports applications such as performance analysis. Labeled training data for algorithms that analyze these cyclic data come at a high annotation cost due to only limited annotations available under laboratory conditions or requiring manual segmentation of the data under less restricted conditions. This paper presents a smart annotation method that reduces this cost of labeling for sensor-based data, which is applicable to data collected outside of strict laboratory conditions. The method uses semi-supervised learning of sections of cyclic data with a known cycle number. A hierarchical hidden Markov model (hHMM) is used, achieving a mean absolute error of 0.041 ± 0.020 s relative to a manually-annotated reference. The resulting model was also used to simultaneously segment and classify continuous, 'in the wild' data, demonstrating the applicability of using hHMM, trained on limited data sections, to label a complete dataset. This technique achieved comparable results to its fully-supervised equivalent. Our semi-supervised method has the significant advantage of reduced annotation cost. Furthermore, it reduces the opportunity for human error in the labeling process normally required for training of segmentation algorithms. It also lowers the annotation cost of training a model capable of continuous monitoring of cycle characteristics such as those employed to analyze the progress of movement disorders or analysis of running technique.
Language	English
Publishing date	2017-10-13
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s17102328
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Thesis: Das Umgangsrecht im Spannungsfeld zwischen Eltern- und Kindesrechten unter besonderer Berücksichtigung der verfassungsrechtlichen Problematik

Hönig, Franziska

(Studien zum Familienrecht ; 1)

2004

Author's details	Franziska Hönig
Series title	Studien zum Familienrecht ; 1
Keywords	Umgangsrecht ; Deutschland
Language	German
Size	XIX, 446 S
Publisher	Kovač
Publishing place	Hamburg
Document type	Book ; Thesis
Thesis / German Habilitation thesis	Univ., Diss.--Göttingen, 2003
Note	Literaturverz. S. 399 - 444
ISBN	3830012160 ; 9783830012160
Database	Former special subject collection: coastal and deep sea fishing

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Article ; Online: Automatic detection of Parkinson's disease in running speech spoken in three different languages.

Orozco-Arroyave, J R / Hönig, F / Arias-Londoño, J D / Vargas-Bonilla, J F / Daqrouq, K / Skodda, S / Rusz, J / Nöth, E

The Journal of the Acoustical Society of America

2016 Volume 139, Issue 1, Page(s) 481–500

Abstract: The aim of this study is the analysis of continuous speech signals of people with Parkinson's disease (PD) considering recordings in different languages (Spanish, German, and Czech). A method for the characterization of the speech signals, based on the ... ...

Abstract	The aim of this study is the analysis of continuous speech signals of people with Parkinson's disease (PD) considering recordings in different languages (Spanish, German, and Czech). A method for the characterization of the speech signals, based on the automatic segmentation of utterances into voiced and unvoiced frames, is addressed here. The energy content of the unvoiced sounds is modeled using 12 Mel-frequency cepstral coefficients and 25 bands scaled according to the Bark scale. Four speech tasks comprising isolated words, rapid repetition of the syllables /pa/-/ta/-/ka/, sentences, and read texts are evaluated. The method proves to be more accurate than classical approaches in the automatic classification of speech of people with PD and healthy controls. The accuracies range from 85% to 99% depending on the language and the speech task. Cross-language experiments are also performed confirming the robustness and generalization capability of the method, with accuracies ranging from 60% to 99%. This work comprises a step forward for the development of computer aided tools for the automatic assessment of dysarthric speech signals in multiple languages.
MeSH term(s)	Adult ; Aged ; Aged, 80 and over ; Area Under Curve ; Czech Republic ; Female ; Germany ; Humans ; Language ; Male ; Middle Aged ; Parkinson Disease/diagnosis ; Parkinson Disease/physiopathology ; Phonetics ; Reading ; Recognition (Psychology) ; Spain ; Speech/physiology ; Speech Acoustics
Language	English
Publishing date	2016-01
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	219231-7
ISSN	1520-8524 ; 0001-4966
ISSN (online)	1520-8524
ISSN	0001-4966
DOI	10.1121/1.4939739
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Saliva molecular inflammatory profiling in female migraine patients responsive to adjunctive cervical non-invasive vagus nerve stimulation: the MOXY Study.

Boström, Azize / Scheele, Dirk / Stoffel-Wagner, Birgit / Hönig, Frigga / Chaudhry, Shafqat R / Muhammad, Sajjad / Hurlemann, Rene / Krauss, Joachim K / Lendvai, Ilana S / Chakravarthy, Krishnan V / Kinfe, Thomas M

Journal of translational medicine

2019 Volume 17, Issue 1, Page(s) 53

Abstract: Background: Rising evidence indicate that oxytocin and IL-1β impact trigemino-nociceptive signaling. Current perspectives on migraine physiopathology emphasize a cytokine bias towards a pro-inflammatory status. The anti-nociceptive impact of oxytocin ... ...

Abstract	Background: Rising evidence indicate that oxytocin and IL-1β impact trigemino-nociceptive signaling. Current perspectives on migraine physiopathology emphasize a cytokine bias towards a pro-inflammatory status. The anti-nociceptive impact of oxytocin has been reported in preclinical and human trials. Cervical non-invasive vagus nerve stimulation (nVNS) emerges as an add-on treatment for the preventive and abortive use in migraine. Less is known about its potential to modulate saliva inflammatory signaling in migraine patients. The rationale was to perform inter-ictal saliva measures of oxytocin and IL-1ß along with headache assessment in migraine patients with 10 weeks adjunctive nVNS compared to healthy controls. Methods: 12 migraineurs and 12 suitably matched healthy control were studied with inter-ictal saliva assay of pro- and anti-neuroinflammatory cytokines using enzyme-linked immuno assay techniques along with assessment of headache severity/frequency and associated functional capacity at baseline and after 10 weeks adjunctive cervical nVNS. Results: nVNS significantly reduced headache severity (VAS), frequency (headache days and total number of attacks) and significantly improved sleep quality compared to baseline (p < 0.01). Inter-ictal saliva oxytocin and IL-1β were significantly elevated pre- as well as post-nVNS compared to healthy controls (p < 0.01) and similarly showed changes that may reflect the observed clinical effects. Conclusions: Our results add to accumulating evidence for a therapeutic efficacy of adjunct cervical non-invasive vagus nerve stimulation in migraine patients. This study failed to provide an evidence-derived conclusion addressed to the predictive value and usefulness of saliva assays due to its uncontrolled study design. However, saliva screening of mediators associated with trigemino-nociceptive traffic represents a novel approach, thus deserve future targeted headache research. Trial registration This study was indexed at the German Register for Clinical Trials (DRKS No. 00011089) registered on 21.09.2016.
MeSH term(s)	Adult ; Aged ; Cervical Vertebrae/innervation ; Depression/etiology ; Female ; Humans ; Inflammation/pathology ; Interleukin-1beta/metabolism ; Middle Aged ; Migraine Disorders/complications ; Migraine Disorders/physiopathology ; Migraine Disorders/therapy ; Oxytocin/metabolism ; Pain ; Quality of Life ; Saliva/metabolism ; Sleep/physiology ; Vagus Nerve Stimulation/adverse effects
Chemical Substances	IL1B protein, human ; Interleukin-1beta ; Oxytocin (50-56-6)
Language	English
Publishing date	2019-02-22
Publishing country	England
Document type	Clinical Trial ; Journal Article ; Observational Study
ISSN	1479-5876
ISSN (online)	1479-5876
DOI	10.1186/s12967-019-1801-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Automatic modelling of depressed speech

Hönig, Florian / Batliner, Anton / Nöth, Elmar / Schnieder, Sebastian / Krajewski, Jarek

Relevant features and relevance of gender

(In: Li, H.; Ching, P. (Ed.), INTERSPEECH 2014. 15th Annual Conference of the International Speech Communication Association, Singapore September 14-18 (S. 1248-1252). Baixas: International Speech Communication Association (ISCA))

2014

Abstract: Depression is an affective disorder characterised by psychomotor retardation; in speech, this shows up in reduction of pitch (variation, range), loudness, and tempo, and in voice qualities different from those of typical modal speech. A similar reduction ...

Title translation	Automatische Modellierung depressiver Sprache: Relevante Merkmale und Relevanz des Geschlechts (DeepL)
Series title	In: Li, H.; Ching, P. (Ed.), INTERSPEECH 2014. 15th Annual Conference of the International Speech Communication Association, Singapore September 14-18 (S. 1248-1252). Baixas: International Speech Communication Association (ISCA)
Abstract	Depression is an affective disorder characterised by psychomotor retardation; in speech, this shows up in reduction of pitch (variation, range), loudness, and tempo, and in voice qualities different from those of typical modal speech. A similar reduction can be observed in sleepy speech (relaxation). In this paper, we employ a small group of acoustic features modelling prosody and spectrum that have been proven successful in the modelling of sleepy speech, enriched with voice quality features, for the modelling of depressed speech within a regression approach. This knowledge-based approach is complemented by and compared with brute-forcing and automatic feature selection. We further discuss gender differences and the contributions of (groups of) features both for the modelling of depression and across depression and sleepiness.
Keywords	Automated Information Processing ; Automatisierte Informationsverarbeitung ; Computational Modeling ; Computermodell ; Major Depression ; Mustererkennung (Computerwissenschaft) ; Mündliche Kommunikation ; Natural Language Processing ; Natürliche Sprachverarbeitung ; Oral Communication ; Pattern Recognition (Computer Science) ; Prosodie ; Prosody ; Schläfrigkeit ; Sleepiness ; Speech Characteristics ; Sprechcharakteristika ; Stimme ; Voice
Language	English
Document type	Article
Database	PSYNDEX

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