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"Emotion-classifying AI isn't anything new, but traditional approaches are supervised, meaning that they ingest training data labeled according to speakers' emotional states. Scientists at Amazon took a different approach recently, which they describe in a paper scheduled to be presented at the International Conference on Acoustics, Speech, and Signal Processing. Rather than sourcing an exhaustively annotated "emotion" corpus to teach a system, they fed an adversarial autoencoder a publicly available data set containing 10,000 utterances from 10 different speakers. The result? The neural network was up to 4% more accurate at judging valence, or emotional value, in peoples' voices." https://developer.amazon.com/fr/blogs/alexa/post/2d8c2128-eec9-44cc-9274-444940eb0a4d/using-adversarial-training-to-recognize-speakers-emotions