{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T11:56:07Z","timestamp":1784030167227,"version":"3.55.0"},"reference-count":66,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,28]],"date-time":"2020-09-28T00:00:00Z","timestamp":1601251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute for Information and Communications Technology Planning and Evaluation","award":["IITP-2020-2018-0-01419"],"award-info":[{"award-number":["IITP-2020-2018-0-01419"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Speech emotion recognition (SER) classifies emotions using low-level features or a spectrogram of an utterance. When SER methods are trained and tested using different datasets, they have shown performance reduction. Cross-corpus SER research identifies speech emotion using different corpora and languages. Recent cross-corpus SER research has been conducted to improve generalization. To improve the cross-corpus SER performance, we pretrained the log-mel spectrograms of the source dataset using our designed visual attention convolutional neural network (VACNN), which has a 2D CNN base model with channel- and spatial-wise visual attention modules. To train the target dataset, we extracted the feature vector using a bag of visual words (BOVW) to assist the fine-tuned model. Because visual words represent local features in the image, the BOVW helps VACNN to learn global and local features in the log-mel spectrogram by constructing a frequency histogram of visual words. The proposed method shows an overall accuracy of 83.33%, 86.92%, and 75.00% in the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), the Berlin Database of Emotional Speech (EmoDB), and Surrey Audio-Visual Expressed Emotion (SAVEE), respectively. Experimental results on RAVDESS, EmoDB, SAVEE demonstrate improvements of 7.73%, 15.12%, and 2.34% compared to existing state-of-the-art cross-corpus SER approaches.<\/jats:p>","DOI":"10.3390\/s20195559","type":"journal-article","created":{"date-parts":[[2020,9,28]],"date-time":"2020-09-28T10:39:58Z","timestamp":1601289598000},"page":"5559","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Fusing Visual Attention CNN and Bag of Visual Words for Cross-Corpus Speech Emotion Recognition"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7564-9264","authenticated-orcid":false,"given":"Minji","family":"Seo","sequence":"first","affiliation":[{"name":"Department of Software Convergence, Soongsil University, 369, Sangdo-ro, Dongjak-gu, Seoul 06978, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1933-7987","authenticated-orcid":false,"given":"Myungho","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Software Convergence, Soongsil University, 369, Sangdo-ro, Dongjak-gu, Seoul 06978, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bachmann, D., Weichert, F., and Rinkenauer, G. 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