18 research outputs found
Uncertainty modeling in affective computing
This disclosure describes techniques that capture the uncertainty in machine-vision based affect (emotion) perception. The techniques are capable of predicting aleatoric, epistemic, and annotation uncertainty. Measures of uncertainty are important to safety-critical and subjective assessment tasks such as those found in the perception of affective expressions
LanSER: Language-Model Supported Speech Emotion Recognition
Speech emotion recognition (SER) models typically rely on costly
human-labeled data for training, making scaling methods to large speech
datasets and nuanced emotion taxonomies difficult. We present LanSER, a method
that enables the use of unlabeled data by inferring weak emotion labels via
pre-trained large language models through weakly-supervised learning. For
inferring weak labels constrained to a taxonomy, we use a textual entailment
approach that selects an emotion label with the highest entailment score for a
speech transcript extracted via automatic speech recognition. Our experimental
results show that models pre-trained on large datasets with this weak
supervision outperform other baseline models on standard SER datasets when
fine-tuned, and show improved label efficiency. Despite being pre-trained on
labels derived only from text, we show that the resulting representations
appear to model the prosodic content of speech.Comment: Presented at INTERSPEECH 202
Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter
The rise in popularity of social networking sites such as Twitter and Facebook has been paralleled by the rise of unwanted, disruptive entities on these networks- — including spammers, malware disseminators, and other content polluters. Inspired by sociologists working to ensure the success of commons and criminologists focused on deterring vandalism and preventing crime, we present the first long-term study of social honeypots for tempting, profiling, and filtering content polluters in social media. Concretely, we report on our experiences via a seven-month deployment of 60 honeypots on Twitter that resulted in the harvesting of 36,000 candidate content polluters. As part of our study, we (1) examine the harvested Twitter users, including an analysis of link payloads, user behavior over time, and followers/following network dynamics and (2) evaluate a wide range of features to investigate the effectiveness of automatic content polluter identification