A text-audio sarcasm detector using back translation and speech synthesis augmentation plus self-attention reports 81.0 F1 on MUStARD, surpassing prior three-modality baselines.
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation
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Building an accurate automatic speech recognition (ASR) system requires a large dataset that contains many hours of labeled speech samples produced by a diverse set of speakers. The lack of such open free datasets is one of the main issues preventing advancements in ASR research. To address this problem, we propose to augment a natural speech dataset with synthetic speech. We train very large end-to-end neural speech recognition models using the LibriSpeech dataset augmented with synthetic speech. These new models achieve state of the art Word Error Rate (WER) for character-level based models without an external language model.
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AMuSeD: An Attentive Deep Neural Network for Multimodal Sarcasm Detection Incorporating Bi-modal Data Augmentation
A text-audio sarcasm detector using back translation and speech synthesis augmentation plus self-attention reports 81.0 F1 on MUStARD, surpassing prior three-modality baselines.