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Aiming at this problem, we propose a new method through the systematical cooperation of Generative Adversarial Network (GAN) and Long Short Term Memory (LSTM). In this method, it utilizes the adversarial training of GAN\u2019s generator and discriminator on speech spectrogram images to implement sufficient sample augmentation. A six-layer convolution neural network (CNN), followed in series by a two-layer LSTM, is designed to extract features from speech spectrograms. For accelerating the training of networks, the parameters of discriminator are transferred to our feature extractor. By the sample augmentation, a well-trained feature extraction network and an efficient classifier could be achieved. The tests and comparisons on two publicly available datasets, i.e., EMO-DB and IEMOCAP, show that our new method is effective, and it is often superior to some state-of-the-art methods. <\/jats:p>","DOI":"10.1142\/s0218126622500736","type":"journal-article","created":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T10:27:07Z","timestamp":1634639227000},"source":"Crossref","is-referenced-by-count":3,"title":["Speech Emotion Recognition on Small Sample Learning by Hybrid WGAN-LSTM Networks"],"prefix":"10.1142","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1030-4169","authenticated-orcid":false,"given":"Cunwei","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology, Sichuan, Chengdu 611731, P. R. 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