Publikationsserver der Ostbayerischen Technischen Hochschule Regensburg
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Evaluating the Impact of Prosody Feature Normalization on the Controllability of Pitch in Speech Synthesis
No full textRecent neural text-to-speech (TTS) models are able to synthesize highly
natural speech signals using deep learning techniques. In practical applications, it can be desirable to have explicit control over the prosody (speech rate, fundamental frequency, and energy) of the synthesized speech. Such controllability can be achieved by adding prosody prediction modules, whose main purpose is to estimate plausible prosody features for each phoneme in the text input. This explicit modeling also allows for changing prosody features at inference time, consequently enabling the adjustment of the prosody in the synthesized audio. In this paper, we evaluate to which extent deliberate manipulation of such prosody features is reflected in the resulting speech audio. We focus particularly on changing the pitch (i.e., fundamental frequency) while applying different normalization strategies
Listener-Oriented Consequences of Predictability-Based Acoustic Adjustment
No full textThis paper investigated whether predictability-based adjustments in production have listener-oriented consequences in perception. By manipulating the acoustic features of a target syllable in different predictability contexts in German, we tested 40 listeners’ perceptual preference for the manipulation. Four source words underwent acoustic modifications on the target syllable. Our results revealed a general preference for the original (unmodified) version over the modified one. However, listeners generally favored the unmodified version more when the source word had a higher predictable context compared to a less predictable one. The results showed that predictability-based adjustments have perceptual consequences and that listeners have predictability-based expectations in perception
Speech Recognition Errors in ASR Engines and Their Impact on Linguistic Analysis in Psychotherapies
No full textModern intervention planning in psychotherapies may benefit from predicting process relevant psychotherapy constructs by automated speech analysis. One essential step is the extraction of relevant linguistic speech markers by ASR engines, which because of highly sensible data, work offline. We analyze transcription errors from NeMo, Whisper, and Wav2Vec2.0, focusing on their impact on linguistic markers usually requiring high quality transcripts. By utilizing part-of-speech tagging, we examine error occurrences among different word types. The Linguistic Inquiry and Word Count (LIWC) software aids in extracting markers. We highlight challenges in transcribing spontaneous speech, prevalent in therapy, and compare results with the Mozilla CommonVoice dataset, which features read speech
Unsupervised emotional pattern recognition using rhythmic and vocal features
No full textIn this study, we address the complex dynamics of emotional speech and
comprehensively examine the integration of rhythmic and vocal features to recognize emotional patterns. Our exploration is conducted using two German emotional corpora: VMEmo and EmoDB. Employing a combination of supervised methods (here linear discriminant analysis, LDA) and unsupervised techniques (here k-means clustering), we aim to uncover nuanced patterns within the emotional speech in these corpora. The application of LDA highlights salient patterns across different feature sets and focuses on the classification of speakers and prosodic characteristics. In addition, k-means clustering uncovers latent structures that reveal subtle mapping between emotions and speech behavior. Our results suggest that it is possible to cluster data based on prosodic behaviors that are influenced by emotional changes. Although precise mapping to the actual clusters derived from emotional labels could not be fully achieved, the results nonetheless reveal a moderate level of success in this investigation
Temporal Super-Resolution, Ground Adjustment, and Advection Correction of Radar Rainfall Using 3-D-Convolutional Neural Networks
No full textWeather radars are highly sophisticated tools for quantitative precipitation estimation (QPE) and provide observations with unmatched spatial representativeness. However, their indirect measurement of precipitation high above ground leads to strong systematic errors compared to direct rain gauge measurements. Additionally, the temporal undersampling from 5-min instantaneous radar measurements requires advection correction. We present ResRadNet, a 3-D-convolutional residual neural network approach, to reduce these errors and, at the same time, increase the temporal resolution of the radar rainfall fields by a 5-min short-range prediction of 1-min time-steps. The network is trained to process spatiotemporal sequences of radar rainfall estimates from a composite product derived from 17 C-band weather radars in Germany. In contrast to previous approaches, we present a method that emphasizes the generation of spatiotemporally consistent and advection-corrected country-wide rainfall maps. Our approach significantly increased the Pearson correlation coefficient (PCC) of the radar product (from 0.63 to 0.74) and decreased the root mean squared error (mse) by 22% when compared to 247 rain gauges at a 5-min resolution. An additional large-scale comparison to eight years of data from 1138 independent manual daily gauges confirmed that the improvement is robust and transferable to new locations. Overall, our study shows the benefits of using 3-D convolutional neural networks (CNNs) for weather radar rainfall estimation to provide 1-min, ground-adjusted, that is, bias-corrected with respect to on-ground sensors, and advection-corrected radar rainfall estimates
Traction with fraction: Strategic IS management in SMEs through Fractional CIOs
No full textSmall and medium-sized enterprises (SMEs) increasingly need to manage nformation technology (IT) effectively in order to remain competitive. However, compared to larger organizations, SMEs often face challenges in terms of resources and employer attractiveness, and regularly do not have the need to employ a Chief Information Officer (CIO) on a full-time basis. To address this issue, a growing number of global experts have begun to provide CIO services on a part-time basis for multiple clients simultaneously. This approach allows SMEs to tap into the expertise of experienced IT leaders at a fraction of the cost and without committing to long-term arrangements. While these professionals, known as “Fractional CIOs”, have proven their value in the field, there has been a lack of academic research on this emerging trend. Therefore, we carried out a comprehensive research project between 2020 and 2023, involving 62 Fractional CIOs from 10 countries. The research produced a definition, different types of engagements, and success factors for Fractional CIOs and their engagements. This paper summarizes these findings for a wider audience of academics and practitioners
CO2-Einsparpotenzial alternativer Bauverfahren am Beispiel von Spezialtiefbau und Rohrgrabenverfüllung
No full textWearable textile antennas: investigation on material variants, fabrication methods, design and application
No full textWith the ongoing miniaturization of wireless devices, the importance of wearable textiles in the antenna segment has increased significantly in recent years. Due to the widespread utilization of wireless body sensor networks for healthcare and ubiquitous applications, the design of wearable antennas offers the possibility of comprehensive monitoring, communication, and energy harvesting and storage. This article reviews a number of properties and benefits to realize comprehensive background information and application ideas for the development of lightweight, compact and low-cost wearable patch antennas. Furthermore, problems and challenges that arise are addressed. Since both electromagnetic and mechanical specifications must be fulfilled, textile and flexible antennas require an appropriate trade-off between materials, antenna topologies, and fabrication methods—depending on the intended application and environmental factors. This overview covers each of the above issues, highlighting research to date while correlating antenna topology, feeding techniques, textile materials, and contacting options for the defined application of wearable planar patch antennas
Theoretical Investigations on a Dynamic Hand Orthosis Based on a Prestressed Compliant Structure with Respect to Stiffness and Wrist-Forces
No full textMany currently available dynamic hand orthoses use articulated connections that have one degree of freedom, such as hinge joints. These orthoses are therefore only able to replicate the multiaxial range of motion of the human hand to a limited extent. A possible solution for replecating the multiaxial movements of the hand is the use of pre-stressed compliant structures as a basis for the orthoses. After a brief description of this concept, the two main first steps by the development, the so called form-finding and the wrist-joint force characterization by hand movements are explained with theoretical analysis based on the static Finite Element Method. The influence of the orthosis parameters, global stiffness and geometric dimensions as well as the influence of the wrist-joint position relative to the orthosis are discussed. Finally, the next planned development steps towards to the first prototype are outlined
Prospective Life Cycle Assessment of Biological Methanation in a Trickle-Bed Pilot Plant and a Potential Scale-Up
No full textThe fluctuating nature of renewable energies results in the need for sustainable storage technologies to defossilize the energy system without other negative consequences for humans and the environment. In this study, a pilot-scale trickle-bed reactor for biological methanation and various scale-up scenarios for 2024 and 2050 were investigated using life cycle assessment. A best- and worst-case scenario for technology development until 2050 was evolved using cross-consistency analysis and a morphological field, based on which the data for the ecological models were determined. The results show that the plant scale-up has a very positive effect on the ecological consequences of methanation. In the best-case scenario, the values are a factor of 23–780 lower than those of the actual plant today. A hot-spot analysis showed that electrolysis operation has an especially large impact on total emissions. The final Monte Carlo simulation shows that the technology is likely to achieve a low global warming potential with a median of 104.0 kg CO2-eq/MWh CH4 and thus can contribute to decarbonization