2019 · 58 citations · 23 references
ComputingLinguisticsComputational LinguisticsFeature EngineeringNatural Language ProcessingAcoustic PhoneticsDeep LearningLanguage ModelingSpeech AcousticsSpeech RecognitionSuprasegmental PhoneticsGerman DialectsIntonation ModelingLearned Feature RepresentationsSleepiness MeasurementSpeech Recognition AcousticsSpeech Sound AnalysisSuprasegmental Speech AnalysisVoice Type Classification
The INTERSPEECH 2019 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Styrian Dialects Sub-Challenge, three types of Austrian-German dialects have to be classified; in the Continuous Sleepiness Sub-Challenge, the sleepiness of a speaker has to be assessed as regression problem; in the Baby Sound Sub-Challenge, five types of infant sounds have to be classified; and in the Orca Activity Sub-Challenge, orca sounds have to be detected.We describe the Sub-Challenges and baseline feature extraction and classifiers, which include data-learnt (supervised) feature representations by the 'usual' ComParE and BoAW features, and deep unsupervised representation learning using the AUDEEP toolkit.
23