arXiv (Cornell University) · 2021 · 34 citations · 25 references
ComputingComputer ScienceLinguisticsComputational LinguisticsMachine LearningNatural Language ProcessingDeep LearningDialogue SystemsLanguage ModelingMultimodal CorporaQuestion AnsweringSpeech RecognitionBinary ClassificationDeep Feature ExtractionDeep Feature LearningEnd-to-end Speech RecognitionEscalationLinguistic Feature AnalysisLinguistic Variation AnalysisSpeech Understanding
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. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AuDeep toolkit, and deep feature extraction from pre-trained CNNs using the Deep Spectrum toolkit; in addition, we add deep end-to-end sequential modelling, and partially linguistic analysis.
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Densely Connected Convolutional Networks
Gao Huang, Zhuang Liu, Laurens van der Maaten et al. · 2017 · 44.7K citations