Search Results for author:
Found 8 papers, 1 papers with code
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.
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.
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.
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.
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
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
