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In this work, we study the hypothesis that speaker identity embeddingsextracted from speech samples may be used for detection and classification ofemotion. In particular, we show that emotions can be effectively identified bylearning speaker identities by use of a 1-D Triplet Convolutional NeuralNetwork (CNN) & Global Style Token (GST) scheme (e.g., DeepTalk Network) andreusing the trained speaker recognition model weights to generate features inthe emotion classification domain. The automatic speaker recognition (ASR)network is trained with VoxCeleb1, VoxCeleb2, and Librispeech datasets with atriplet training loss function using speaker identity labels. Using an SupportVector Machine (SVM) classifier, we map speaker identity embeddings intodiscrete emotion categories from the CREMA-D, IEMOCAP, and MSP-Podcastdatasets. On the task of speech emotion detection, we obtain 80.8% ACC withacted emotion samples from CREMA-D, 81.2% ACC with semi-natural emotion samplesin IEMOCAP, and 66.9% ACC with natural emotion samples in MSP-Podcast. We alsopropose a novel two-stage hierarchical classifier (HC) approach whichdemonstrates +2% ACC improvement on CREMA-D emotion samples. Through this work,we seek to convey the importance of holistically modeling intra-user variationwithin audio samples

Is Style All You Need? Dependencies Between Emotion and GST-based Speaker Recognition