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20162022
most citedUnacceptable, where is my privacy? Exploring Accidental Triggers of Smart Speakers

16 citations · 44 across the 18 of their papers we have counts for

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Showing 2020Show all

7 papers · 1 filter

eess.AS2020

Non-intrusive speech intelligibility prediction using automatic speech recognition derived measures

Mahdie Karbasi, Stefan Bleeck, Dorothea Kolossa

The estimation of speech intelligibility is still far from being a solved problem. Especially one aspect is problematic: most of the standard models require a clean reference signa…

cs.CL20205 cited

Deep Bayes Factor Scoring for Authorship Verification

Benedikt Boenninghoff, Julian Rupp, Robert M. Nickel +1

The PAN 2020 authorship verification (AV) challenge focuses on a cross-topic/closed-set AV task over a collection of fanfiction texts. Fanfiction is a fan-written extension of a st…

cs.CR202016 cited

Unacceptable, where is my privacy? Exploring Accidental Triggers of Smart Speakers

Lea Schönherr, Maximilian Golla, Thorsten Eisenhofer +3

Voice assistants like Amazon's Alexa, Google's Assistant, or Apple's Siri, have become the primary (voice) interface in smart speakers that can be found in millions of households.…

eess.AS20205 cited

Multimodal Integration for Large-Vocabulary Audio-Visual Speech Recognition

Wentao Yu, Steffen Zeiler, Dorothea Kolossa

For many small- and medium-vocabulary tasks, audio-visual speech recognition can significantly improve the recognition rates compared to audio-only systems. However, there is still…

cs.LG2020

Variational Autoencoder with Embedded Student- Mixture Model for Authorship Attribution

Benedikt Boenninghoff, Steffen Zeiler, Robert M. Nickel +1

Traditional computational authorship attribution describes a classification task in a closed-set scenario. Given a finite set of candidate authors and corresponding labeled texts,…

eess.AS2020

Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification

Sina Däubener, Lea Schönherr, Asja Fischer +1

Machine learning systems and also, specifically, automatic speech recognition (ASR) systems are vulnerable against adversarial attacks, where an attacker maliciously changes the in…