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Emotion Intensity and its Control for Emotional Voice Conversion ...
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Dawn of the transformer era in speech emotion recognition: closing the valence gap ...
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Probing Speech Emotion Recognition Transformers for Linguistic Knowledge ...
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An Improved StarGAN for Emotional Voice Conversion: Enhancing Voice Quality and Data Augmentation ...
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The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates ...
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On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era ...
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Multistage linguistic conditioning of convolutional layers for speech emotion recognition ...
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A Comparison of Acoustic and Linguistics Methodologies for Alzheimer's Dementia Recognition
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In: http://infoscience.epfl.ch/record/284990 (2021)
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The voice of COVID-19: Acoustic correlates of infection in sustained vowels
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In: J Acoust Soc Am (2021)
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COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis
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In: Front Digit Health (2021)
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Abstract:
At the time of writing this article, the world population is suffering from more than 2 million registered COVID-19 disease epidemic-induced deaths since the outbreak of the corona virus, which is now officially known as SARS-CoV-2. However, tremendous efforts have been made worldwide to counter-steer and control the epidemic by now labelled as pandemic. In this contribution, we provide an overview on the potential for computer audition (CA), i.e., the usage of speech and sound analysis by artificial intelligence to help in this scenario. We first survey which types of related or contextually significant phenomena can be automatically assessed from speech or sound. These include the automatic recognition and monitoring of COVID-19 directly or its symptoms such as breathing, dry, and wet coughing or sneezing sounds, speech under cold, eating behaviour, sleepiness, or pain to name but a few. Then, we consider potential use-cases for exploitation. These include risk assessment and diagnosis based on symptom histograms and their development over time, as well as monitoring of spread, social distancing and its effects, treatment and recovery, and patient well-being. We quickly guide further through challenges that need to be faced for real-life usage and limitations also in comparison with non-audio solutions. We come to the conclusion that CA appears ready for implementation of (pre-)diagnosis and monitoring tools, and more generally provides rich and significant, yet so far untapped potential in the fight against COVID-19 spread.
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Keyword:
Digital Health
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URL: https://doi.org/10.3389/fdgth.2021.564906 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521916/
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AI-based Human Audio Processing for COVID-19: A Comprehensive Overview
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In: Pattern Recognit (2021)
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Face Mask Recognition from Audio: The MASC Database and an Overview on the Mask Challenge
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In: Pattern Recognit (2021)
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Audio, Speech, Language, & Signal Processing for COVID-19: A Comprehensive Overview ...
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Speaker trait characterization in web videos: Uniting speech, language, and facial features
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In: Proceedings of the 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) ; 3647-3651 ; International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) ; 38 (2020)
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On-line emotion recognition in a 3-D activation-valence-time continuum using acoustic and linguistic cues
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Discrimination of speech and non-linguistic vocalizations by non-negative matrix factorization
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A comparison of acoustic and linguistics methodologies for Alzheimer's dementia recognition
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"The Godfather" vs. "Chaos": comparing linguistic analysis based on on-line knowledge sources and Bags-of-N-Grams for movie review valence estimation
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Speaker independent emotion recognition by early fusion of acoustic and linguistic features within ensembles
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On the influence of phonetic content variation for acoustic emotion recognition
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