arXiv subjects

Ramona Schoedel

Publications and source records attributed to Ramona Schoedel.

3 recordsLinked to original sources

arXiv2026

Collecting Prosody in the Wild: A Content-Controlled, Privacy-First Smartphone Protocol and Empirical Evaluation

Collecting everyday speech data for prosodic analysis is challenging due to the confounding of prosody and semantics, privacy constraints, and participant compliance. We introduce and empirically evaluate a content-controlled, privacy-first smartphone protocol that uses scripted read-aloud sentences to standardize lexical content (including prompt valence) while capturing naturalistic variation in prosodic delivery. The protocol performs on-device prosodic feature extraction, deletes raw audio immediately, and transmits only derived features for analysis. We deployed the protocol in a large study (N = 560; 9,877 recordings), evaluated compliance and data quality, and conducted diagnostic prediction tasks on the extracted features, predicting self-reported speaker sex and momentary affective states (valence, arousal). We discuss implications and directions for advancing and deploying the protocol.

cs.HC↗

arXiv2024

Putting Language into Context Using Smartphone-Based Keyboard Logging

While the study of language as typed on smartphones offers valuable insights, existing data collection methods often fall short in providing contextual information and ensuring user privacy. We present a privacy-respectful approach - context-enriched keyboard logging - that allows for the extraction of contextual information on the user's input motive, which is meaningful for linguistics, psychology, and behavioral sciences. In particular, with our approach, we enable distinguishing language contents by their channel (i.e., comments, messaging, search inputs). Filtering by channel allows for better pre-selection of data, which is in the interest of researchers and improves users' privacy. We demonstrate our approach on a large-scale six-month user study (N=624) of language use in smartphone interactions in the wild. Finally, we highlight the implications for research on language use in human-computer interaction and interdisciplinary contexts.

cs.HC↗

arXiv2019

Component-Wise Boosting of Targets for Multi-Output Prediction

Multi-output prediction deals with the prediction of several targets of possibly diverse types. One way to address this problem is the so called problem transformation method. This method is often used in multi-label learning, but can also be used for multi-output prediction due to its generality and simplicity. In this paper, we introduce an algorithm that uses the problem transformation method for multi-output prediction, while simultaneously learning the dependencies between target variables in a sparse and interpretable manner. In a first step, predictions are obtained for each target individually. Target dependencies are then learned via a component-wise boosting approach. We compare our new method with similar approaches in a benchmark using multi-label, multivariate regression and mixed-type datasets.

stat.ML↗