FG Kommunikationstechnik
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Vector symbolic architectures (VSA) are a viable approach for the hyperdimensional representation of symbolic data, such as documents, syntactic structures, or semantic frames. We present a rigorous mathematical framework for the representation of phrase structure trees and parse trees of context-free grammars (CFG) in Fock space, i.e. infinite-dimensional Hilbert space as being used in quantum field theory. We define a novel normal form for CFG by means of term algebras. Using a recently developed software toolbox, called FockBox, we construct Fock space representations for the trees built up by a CFG left-corner (LC) parser. We prove a universal representation theorem for CFG term algebras in Fock space and illustrate our findings through a low-dimensional principal component projection of the LC parser state. Our approach could leverage the development of VSA for explainable artificial intelligence (XAI) by means of hyperdimensional deep neural computation.
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.
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.
Metaphors involving motion and forces are a source of inspiration for understanding tonal music and tonal harmonies since ancient times. Starting with the rise of quantum cognition, the modern interactional conception of forces as developed in gauge theory has recently entered the field of theoretical musicology. We develop a gauge model of tonal attraction based on SU(2) symmetry. This model comprises two earlier attempts, the phase model grounded on U(1) gauge symmetry, and the spatial deformation model derived from SO(2) gauge symmetry. In the neutral, force-free case both submodels agree and generate the same predictions as a simple qubit approach. However, there are several differences in the force-driven case. It is claimed that the deformation model gives a proper description of static tonal attraction. The full model combines the deformation model with the phase model through SU(2) gauge symmetry and unifies static and dynamic tonal attraction.
Das Fachgebiet der Kognitiven Technischen Systeme zeichnet sich durch einen hohen Grad an Interdisziplinarität (z. B. Kenntnisse auf den Gebieten Biologie, Psychologie, Informatik und den Ingenieurwissenschaften) aus. Nach wie vor besteht Bedarf an einer methodischen Darstellung des Fachgebietes, bei der die theoretische Durchdringung von Zusammenhängen zwischen den verschiedenen Wissensgebieten zusätzlich durch Anschaulichkeit unterstützt wird. Die vorliegende Arbeit stellt einen Fortschrittsbericht zur Realisierung eines Forschungs- und Experimentiersystems dar, mit dem wir dieses Anliegen unterstützen und über das wir erstmals konzeptionell in [1] berichtet haben. In diesem Beitrag folgen wir einem integrativen Ansatz zur Entwicklung einer kognitiven Architektur, mit der unter Verwendung repräsentationaler Datenstrukturen adaptives Verhalten auf verschiedenen Zeitskalen sowie zwei wichtige Verhaltensprogramme für das Problemlösen (Objektfindung, Exploration) auf der gemeinsamen Grundlage von Markov-Entscheidungsprozessen umgesetzt werden. Mit einem Kurzbericht zum Entwicklungsstand der physikalischen Experimentierumgebung und einer Zusammenfassung der bislang erreichten Ergebnisse beschließen wir den diesjährigen Beitrag.
Zunächst möchten wir in das Thema einführen, indem wir kurz auf
gegenwärtige Probleme und zukünftige Möglichkeiten von Sprachassistenten eingehen. Es folgen grundlegende Überlegungen zur Semantik und zwei fundamentale Erkenntnisse aus der Biosemiotik und dem Konstruktivismus. Danach fokussieren wir uns auf den Spracherwerbsprozess. Hierbei kommt ein Petrinetz zum Einsatz, das einem elementaren, bereits in früheren Arbeiten vorgestellten, Verstärkungsalgorithmus folgt. Dem schließt sich eine Demonstration des Spracherwerbs anhand eines Beispiels aus dem Smart Car-Bereich an.
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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).
We present an approach for ultrasonic non-destructive testing of adhesive bonding employing unsupervised machine learning with autoencoders.The models are trained exclusively on the features derived from pulse-echo ultrasonic signals on a specimen with good adhesive bonding and tested on another specimen with artificially added defects.The resulting pseudo-probabilities indicating anomalies are visualized and presented along to the C-scan of the same specimen. As a result, we achieved improved representation of the defects, allowing their automatic and reliable detection.