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In this work, we train fully convolutional networks to detect anger inspeech. Since training these deep architectures requires large amounts of dataand the size of emotion datasets is relatively small, we use transfer learning.However, unlike previous approaches that use speech or emotion-based tasks forthe source model, we instead use SoundNet, a fully convolutional neural networktrained multimodally on a massive video dataset to classify audio, withground-truth labels provided by vision-based classifiers. As a result oftransfer learning from SoundNet, our trained anger detection model improvesperformance and generalizes well on a variety of acted, elicited, and naturalemotional speech datasets. We also test the cross-lingual effectiveness of ourmodel by evaluating our English-trained model on Mandarin Chinese speechemotion data. Furthermore, our proposed system has low latency suitable forreal-time applications, only requiring 1.2 seconds of audio to make a reliableclassification.

Transfer Learning From Sound Representations For Anger Detection in Speech