most citedOn the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era

9 citations · 25 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SD20211 cited

DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing from Decentralised Data

Shahin Amiriparian, Tobias Hübner, Maurice Gerczuk +2

Deep neural speech and audio processing systems have a large number of trainable parameters, a relatively complex architecture, and require a vast amount of training data and compu…

cs.SD20219 cited

On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era

Shahin Amiriparian, Artem Sokolov, Ilhan Aslan +9

Text encodings from automatic speech recognition (ASR) transcripts and audio representations have shown promise in speech emotion recognition (SER) ever since. Yet, it is challengi…

cs.SD20217 cited

EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognition

Maurice Gerczuk, Shahin Amiriparian, Sandra Ottl +1

In this manuscript, the topic of multi-corpus Speech Emotion Recognition (SER) is approached from a deep transfer learning perspective. A large corpus of emotional speech data, Emo…

eess.AS20212 cited

The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates

Björn W. Schuller, Anton Batliner, Christian Bergler +21

The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the CO…

eess.AS20206 cited

A Novel Fusion of Attention and Sequence to Sequence Autoencoders to Predict Sleepiness From Speech

Shahin Amiriparian, Pawel Winokurow, Vincent Karas +3

Motivated by the attention mechanism of the human visual system and recent developments in the field of machine translation, we introduce our attention-based and recurrent sequence…