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Found 8 papers, 4 papers with code
Fatigue Prediction in Outdoor Running Conditions using Audio Data
Although running is a common leisure activity and a core training regiment for several athletes, between $29\%$ and $79\%$ of runners sustain an overuse injury each year.
A Summary of the ComParE COVID-19 Challenges
The COVID-19 pandemic has caused massive humanitarian and economic damage.
A Machine Learning Framework for Automatic Prediction of Human Semen Motility
Finally, a linear SVR is trained on this feature representation.
DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing from Decentralised Data
By obtaining state-of-the-art results on a set of paralinguistics tasks, we demonstrate the suitability of the proposed transfer learning approach for embedded audio signal processing, even when data is scarce.
On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era
Text encodings from automatic speech recognition (ASR) transcripts and audio representations have shown promise in speech emotion recognition (SER) ever since.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognition
The corpus is then utilised to create a novel framework for multi-corpus speech emotion recognition, namely EmoNet.
The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates
The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified.
A Novel Fusion of Attention and Sequence to Sequence Autoencoders to Predict Sleepiness From Speech
On the development partition of the data, we achieve Spearman's correlation coefficients of . 324, . 283, and . 320 with the targets on the Karolinska Sleepiness Scale by utilising attention and non-attention autoencoders, and the fusion of both autoencoders' representations, respectively.
