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1
Emotion Intensity and its Control for Emotional Voice Conversion ...
Zhou, Kun; Sisman, Berrak; Rana, Rajib. - : arXiv, 2022
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2
Dawn of the transformer era in speech emotion recognition: closing the valence gap ...
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3
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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5
The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates ...
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6
On the Impact of Word Error Rate on Acoustic-Linguistic Speech Emotion Recognition: An Update for the Deep Learning Era ...
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7
Multistage linguistic conditioning of convolutional layers for speech emotion recognition ...
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8
A Comparison of Acoustic and Linguistics Methodologies for Alzheimer's Dementia Recognition
In: http://infoscience.epfl.ch/record/284990 (2021)
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9
The voice of COVID-19: Acoustic correlates of infection in sustained vowels
In: J Acoust Soc Am (2021)
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10
COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis
In: Front Digit Health (2021)
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11
AI-based Human Audio Processing for COVID-19: A Comprehensive Overview
In: Pattern Recognit (2021)
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12
Face Mask Recognition from Audio: The MASC Database and an Overview on the Mask Challenge
In: Pattern Recognit (2021)
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13
Audio, Speech, Language, & Signal Processing for COVID-19: A Comprehensive Overview ...
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14
Speaker trait characterization in web videos: Uniting speech, language, and facial features
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)
Abstract: We present a multi-modal approach to speaker characterization using acoustic, visual and linguistic features. Full realism is provided by evaluation on a database of real-life web videos and automatic feature extraction including face and eye detection, and automatic speech recognition. Different segmentations are evaluated for the audio and video streams, and the statistical relevance of Linguistic Inquiry and Word Count (LIWC) features is confirmed. In the result, late multimodal fusion delivers 73, 92 and 73% average recall in binary age, gender and race classification on unseen test subjects, outperforming the best single modalities for age and race.
Keyword: angewandte Wissenschaften; Applied Sciences; audiovisual media; audiovisuelle Medien; Aufzeichnung; computational linguistics; computational paralinguistics; Computerlinguistik; Evaluation; Experiment; Internet; Linguistic Inquiry and Word Count; LIWC; multi-modal fusion; Natural Science and Engineering; Naturwissenschaften; recording; Science; social media; Soziale Medien; speaker classification; Technik(wissenschaften); Video; video clip; Video-Clip
URL: http://nbn-resolving.org/urn:nbn:de:0168-ssoar-66084-2
https://doi.org/10.1109/ICASSP.2013.6638338
https://www.ssoar.info/ssoar/handle/document/66084
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15
On-line emotion recognition in a 3-D activation-valence-time continuum using acoustic and linguistic cues
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16
Discrimination of speech and non-linguistic vocalizations by non-negative matrix factorization
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17
A comparison of acoustic and linguistics methodologies for Alzheimer's dementia recognition
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18
"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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19
Speaker independent emotion recognition by early fusion of acoustic and linguistic features within ensembles
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20
On the influence of phonetic content variation for acoustic emotion recognition
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