Angular correlation of electrons and positrons in internal pair conversion

The angular distribution of electrons and positrons emitted in internal pair conversion is calculated. Coulomb-distorted waves are used as electron wave functions. Nuclear transitions of various multipolarities L>0 and of magnetic (ML) and of electric (EL) type are considered as well as E0 conversion. Analytical expressions for the angular correlation are derived, which are evaluated numerically assuming a finite extension of the nucleus and, for the EL and ML conversion, also in the point-nucleus approximation. The calculated angular correlations are compared with results obtained within the Born approximation and, for the E0 case, with experimental data

Development of a low-cost EMG-data acquisition armband to control an above-elbow prosthesis

This work presents the development and implementation of an armband for EMG-data acquisition of the upper arm. The developed prototype serves for investigations on EMG-signal processing with the final objective of reliably controlling a prosthesis for a transhumeral amputee. To make the product available for a greatest possible number of people one of the main characteristics will be a design of very low cost. A focus is put on the electric circuit design. In order to manufacture the prototype without difficulties, it is exclusively designed with components, which are available on the Ecuadorian market. The development is based on previous investigations and approaches of other researchers. With help of electric circuit simulations, the design was adapted and optimized step by step and a reliable low-noise circuit was established. All components are arranged on printed circuit boards in a way to keep the device as small as possible. To optimally avoid noise the length of the connection from the electrodes to the amplifier is minimized. Compact 3D-printed housings cover all the electric components. All housings consist of only two parts and are intuitive to assemble. Holes in the bottom offer space to fix electrodes via a snap-fastener connection. 3D-printed elastic straps are designed to connect the subsystems and hold the device in place. The armband including two sensors weighs 92 g and is capable of measuring two muscles with a bipolar EMG-setting each, sharing one reference electrode, which is aligned on the side of the upper arm between the biceps and triceps muscles. The amplification of the sensors is adjustable individually by potentiometers, facilitating a gain factor range of 211 to 2016 V/V. The data is recorded by a microcontroller board and send to a computer for processing via wire or Bluetooth. For wireless operation, rechargeable batteries are integrated. Test measurements on an able-bodied human prove the functionality of the device

RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation

We present state-of-the-art automatic speech recognition (ASR) systems employing a standard hybrid DNN/HMM architecture compared to an attention-based encoder-decoder design for the LibriSpeech task. Detailed descriptions of the system development, including model design, pretraining schemes, training schedules, and optimization approaches are provided for both system architectures. Both hybrid DNN/HMM and attention-based systems employ bi-directional LSTMs for acoustic modeling/encoding. For language modeling, we employ both LSTM and Transformer based architectures. All our systems are built using RWTHs open-source toolkits RASR and RETURNN. To the best knowledge of the authors, the results obtained when training on the full LibriSpeech training set, are the best published currently, both for the hybrid DNN/HMM and the attention-based systems. Our single hybrid system even outperforms previous results obtained from combining eight single systems. Our comparison shows that on the LibriSpeech 960h task, the hybrid DNN/HMM system outperforms the attention-based system by 15% relative on the clean and 40% relative on the other test sets in terms of word error rate. Moreover, experiments on a reduced 100h-subset of the LibriSpeech training corpus even show a more pronounced margin between the hybrid DNN/HMM and attention-based architectures.Comment: Proceedings of INTERSPEECH 201

Efficient Utilization of Large Pre-Trained Models for Low Resource ASR

Unsupervised representation learning has recently helped automatic speech recognition (ASR) to tackle tasks with limited labeled data. Following this, hardware limitations and applications give rise to the question how to take advantage of large pre-trained models efficiently and reduce their complexity. In this work, we study a challenging low resource conversational telephony speech corpus from the medical domain in Vietnamese and German. We show the benefits of using unsupervised techniques beyond simple fine-tuning of large pre-trained models, discuss how to adapt them to a practical telephony task including bandwidth transfer and investigate different data conditions for pre-training and fine-tuning. We outperform the project baselines by 22% relative using pretraining techniques. Further gains of 29% can be achieved by refinements of architecture and training and 6% by adding 0.8 h of in-domain adaptation data.Comment: Accepted at ICASSP SASB 202

Monitoring of a polar plasma convection event with GPS

When L-band radio waves of space based systems such as Global Positioning System (GPS) travel trough the ionosphere and plasmasphere their ray paths are perturbed due to the free electrons. Since the last decade these integrated measurements are used to map the ionosphere for navigational and scientific investigations. In November 2001 a polar plasma convection like ionospheric event has been recognised in vertical TEC maps produced with GPS data. This event on the one hand is shortly compared with the behaviour of the Interplanetary Magnetic Field (IMF) to which it may be related according to former publications. On the other hand the 3-dimensional tomography applying also GPS data is tested on its capability to reconstruct this ionospheric event in the European sector. The different mappings of the two monitoring methods are compared.Wenn L-Band-Radiowellen raumgestützter Navigationssysteme wie das Global Positioning System (GPS) die Ionosphäre oder Plasmasphäre durchlaufen, werden Ihre Strahlwege durch die freien Elektronen verändert. Seit dem letzten Jahrzehnt verwendet man diese integrierten Messungen, um die Ionosphäre im Interesse der Navigation und der Wissenschaft abzubilden. Am Beispiel eines Ereignisses vom November 2001 wurde eine polare Plasmakonvektion in der Ionosphäre durch vertikale TEC –Karten (Total Electron Content), die ebenfalls mit Hilfe von GPS Daten erstellt werden, abgebildet. Einerseits wurde das Ereignis der Plasmakonvektion mit dem Verhalten des Interplanetaren Magnetischen Feldes (IMF) kurz verglichen und auf ihren Zusammenhang hin untersucht. Auf der anderen Seite wurde anhand dieses Ereignisses die Methode einer über den europäischen Raum aufgespannten auf GPS–Daten basierenden 3-dimensionale Tomographie auf ihre Reproduzierbarkeit hin geprüft. Die zwei verschiedenen Methoden des Ionosphärenmonitorings werden verglichen

Analyzing And Improving Neural Speaker Embeddings for ASR

Neural speaker embeddings encode the speaker's speech characteristics through a DNN model and are prevalent for speaker verification tasks. However, few studies have investigated the usage of neural speaker embeddings for an ASR system. In this work, we present our efforts w.r.t integrating neural speaker embeddings into a conformer based hybrid HMM ASR system. For ASR, our improved embedding extraction pipeline in combination with the Weighted-Simple-Add integration method results in x-vector and c-vector reaching on par performance with i-vectors. We further compare and analyze different speaker embeddings. We present our acoustic model improvements obtained by switching from newbob learning rate schedule to one cycle learning schedule resulting in a ~3% relative WER reduction on Switchboard, additionally reducing the overall training time by 17%. By further adding neural speaker embeddings, we gain additional ~3% relative WER improvement on Hub5'00. Our best Conformer-based hybrid ASR system with speaker embeddings achieves 9.0% WER on Hub5'00 and Hub5'01 with training on SWB 300h.Comment: Accepted at ITG Speech Communications 202

Comparison of electron density profiles in the ionosphere from ionospheric assimilations of GPS, CHAMP profiling and ionosondes over Europe

GPS integrated Total Electron Content measurements received at the ground or in space are used for tomographic reconstruction of the ionospheric electron density distribution. The IRI/GCPM model is used as initialisation of the tomographic MART algorithm. During the procedure GPS TEC data are iteratively assimilated to the model. To test the potential of the reconstruction, electron density profiles from IRI/GCPM and the assimilation are compared with ionosonde measurements and CHAMP radio occultation profiles for dates during the HIRAC campaign in April 2001. All profiling methods show electron density values of similar magnitude. It is shown that including TEC GPS data corrects the model towards the ionosonde measurements.Integrale Messungen der Elektronendichte aus GPS-Boden- sowie Radio-Okkultations-Messungen bilden die Datenbasis der hier vorgestellten 3-dimensionalen Tomographie der ionosphärischen Elektronendichteverteilung. Zur Initialisierung des verwendeten iterativen MART Algorithmus wird das IRI/GCPM Modell verwendet, wobei das Modell während der Iteration sukzessiv an die Messdaten angepasst wird. Um das Potential des Verfahrens abzuschätzen, werden Elektronendichteprofile des IRI/GCPM Modells und der Rekonstruktion mit Ionosondenmessungen und CHAMP Okkultationsprofilen verglichen. Dafür wurden Messungen während der HIRAC Kampagne im April 2001 genutzt. Alle hier gezeigten Profilableitungen geben Elektronendichtewerte der selben Größe wieder. Eine Annäherung des IRI/GCPM Modells an die Messwerte der Ionosonde durch die Assimilation der TEC GPS Daten wird gezeigt

The Influence of Personal School Experience in Biology Classes on the Beliefs of Students in University Teacher Education

Teachers’ pedagogical beliefs are thought to play a prominent role in determining teacher behavior. In contrast to other professions, pedagogical beliefs of teachers and students in teacher education are widely influenced by personal experiences gained in school, which has been referred to as “apprenticeship of observation” (Lortie, 1975, p. 61).It can be assumed that student teachers already enter teacher education with a relatively firm set of beliefs about teaching. In our study, N = 280 student teachers in biology were asked to recall their own biology classes in school, employing a frame of reference provided by national standards for biology education in Germany. First, a factor analysis was conducted on students’ responses. This analysis yielded four aspects (factors) according to which students’ recall of their own biology classes in school is structured. Next, students were clustered into four biography types by means of their parameter values on the four factors. Students’ beliefs about how biology should be taught are influenced by their biography. Our findings thus provide evidence for the influence of school biography on pedagogical beliefs in the field of biology education; however, this influence does not point in a uniform direction. When comparing university freshmen to more advanced student teachers, only one out of the four belief aspects was affected by students’ progress in their university studies. A practical implication is that teacher educators are well-advised to incorporate their students’ past experiences into the contents of their courses

Enhancing and Adversarial: Improve ASR with Speaker Labels

ASR can be improved by multi-task learning (MTL) with domain enhancing or domain adversarial training, which are two opposite objectives with the aim to increase/decrease domain variance towards domain-aware/agnostic ASR, respectively. In this work, we study how to best apply these two opposite objectives with speaker labels to improve conformer-based ASR. We also propose a novel adaptive gradient reversal layer for stable and effective adversarial training without tuning effort. Detailed analysis and experimental verification are conducted to show the optimal positions in the ASR neural network (NN) to apply speaker enhancing and adversarial training. We also explore their combination for further improvement, achieving the same performance as i-vectors plus adversarial training. Our best speaker-based MTL achieves 7\% relative improvement on the Switchboard Hub5'00 set. We also investigate the effect of such speaker-based MTL w.r.t. cleaner dataset and weaker ASR NN.Comment: accepted at ICASSP 202