Search Results for author:
Found 30 papers, 15 papers with code
Hierarchy-aware Learning of Sequential Tool Usage via Semi-automatically Constructed Taxonomies
When repairing a device, humans employ a series of tools that corresponds to the arrangement of the device components.
DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distribution
Adversarial attacks can mislead automatic speech recognition (ASR) systems into predicting an arbitrary target text, thus posing a clear security threat.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
RubCSG at SemEval-2022 Task 5: Ensemble learning for identifying misogynous MEMEs
This work presents an ensemble system based on various uni-modal and bi-modal model architectures developed for the SemEval 2022 Task 5: MAMI-Multimedia Automatic Misogyny Identification.
Robustifying automatic speech recognition by extracting slowly varying features
We perform an empirical analysis of hybrid ASR models trained on data pre-processed in such a way.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Federated Learning in ASR: Not as Easy as You Think
We discuss the outcomes of these systems, which both show great similarities and only small improvements, pointing to a need for a deeper understanding of federated learning for speech recognition.
Large-vocabulary Audio-visual Speech Recognition in Noisy Environments
To address the inherent difficulties, we propose a new fusion strategy: a recurrent integration network is trained to fuse the state posteriors of multiple single-modality models, guided by a set of model-based and signal-based stream reliability measures.
O2D2: Out-Of-Distribution Detector to Capture Undecidable Trials in Authorship Verification
The PAN 2021 authorship verification (AV) challenge is part of a three-year strategy, moving from a cross-topic/closed-set AV task to a cross-topic/open-set AV task over a collection of fanfiction texts.
Self-Calibrating Neural-Probabilistic Model for Authorship Verification Under Covariate Shift
We are addressing two fundamental problems in authorship verification (AV): Topic variability and miscalibration.
PILOT: Introducing Transformers for Probabilistic Sound Event Localization
Sound event localization aims at estimating the positions of sound sources in the environment with respect to an acoustic receiver (e. g. a microphone array).
Fusing information streams in end-to-end audio-visual speech recognition
While audio-visual speech recognition can significantly improve the recognition rate of end-to-end models in such poor conditions, it is not obvious how to best utilize any available information on acoustic and visual signal quality and reliability in these models.
Unsupervised Classification of Voiced Speech and Pitch Tracking Using Forward-Backward Kalman Filtering
The detection of voiced speech, the estimation of the fundamental frequency, and the tracking of pitch values over time are crucial subtasks for a variety of speech processing techniques.
Exploiting Attention-based Sequence-to-Sequence Architectures for Sound Event Localization
Herein, attentions allow for capturing temporal dependencies in the audio signal by focusing on specific frames that are relevant for estimating the activity and direction-of-arrival of sound events at the current time-step.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial Domain
Estimating the positions of multiple speakers can be helpful for tasks like automatic speech recognition or speaker diarization.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Dompteur: Taming Audio Adversarial Examples
In this paper we propose a different perspective: We accept the presence of adversarial examples against ASR systems, but we require them to be perceivable by human listeners.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution
In this work, we propose a probabilistic autoencoding framework to deal with this supervised classification task.
VenoMave: Targeted Poisoning Against Speech Recognition
In a more realistic scenario, when the target audio waveform is played over the air in different rooms, VENOMAVE maintains a success rate of up to 73. 3%.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Non-intrusive speech intelligibility prediction using automatic speech recognition derived measures
The estimation of speech intelligibility is still far from being a solved problem.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Deep Bayes Factor Scoring for Authorship Verification
The PAN 2020 authorship verification (AV) challenge focuses on a cross-topic/closed-set AV task over a collection of fanfiction texts.
Variational Autoencoder with Embedded Student-$t$ Mixture Model for Authorship Attribution
In this work, we are extending the Gaussian model for the VAE to a Student-$t$ model, which allows for an independent control of the "heaviness" of the respective tails of the implied probability densities.
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification
The neural networks for uncertainty quantification simultaneously diminish the vulnerability to the attack, which is reflected in a lower recognition accuracy of the malicious target text in comparison to a standard hybrid ASR system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
MyFixit: An Annotated Dataset, Annotation Tool, and Baseline Methods for Information Extraction from Repair Manuals
In this paper, we, therefore, focus on information extraction (IE) from the instructional text in repair manuals.
Leveraging Frequency Analysis for Deep Fake Image Recognition
Based on this analysis, we demonstrate how the frequency representation can be used to identify deep fake images in an automated way, surpassing state-of-the-art methods.
On Neural Phone Recognition of Mixed-Source ECoG Signals
The emerging field of neural speech recognition (NSR) using electrocorticography has recently attracted remarkable research interest for studying how human brains recognize speech in quiet and noisy surroundings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Explainable Authorship Verification in Social Media via Attention-based Similarity Learning
Authorship verification is the task of analyzing the linguistic patterns of two or more texts to determine whether they were written by the same author or not.
Speaker-adapted neural-network-based fusion for multimodal reference resolution
Humans use a variety of approaches to reference objects in the external world, including verbal descriptions, hand and head gestures, eye gaze or any combination of them.
Similarity Learning for Authorship Verification in Social Media
Authorship verification tries to answer the question if two documents with unknown authors were written by the same author or not.
Imperio: Robust Over-the-Air Adversarial Examples for Automatic Speech Recognition Systems
In this paper, we demonstrate the first algorithm that produces generic adversarial examples, which remain robust in an over-the-air attack that is not adapted to the specific environment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Joining Sound Event Detection and Localization Through Spatial Segregation
This work presents an approach that robustly binds localization with the detection of sound events in a binaural robotic system.
Sound Audio and Speech Processing
Audiovisual Speaker Tracking using Nonlinear Dynamical Systems with Dynamic Stream Weights
This paper presents a framework that extends the well-established theory of nonlinear dynamical systems with the notion of dynamic stream weights for an arbitrary number of sensory observations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding
We use this backpropagation to learn the degrees of freedom for the adversarial perturbation of the input signal, i. e., we apply a psychoacoustic model and manipulate the acoustic signal below the thresholds of human perception.
Cryptography and Security Sound Audio and Speech Processing
