2018/01/19 by Siddique Latif, Latif, Siddique, Rajib Rana +7
Computer Science · Psychology · #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #Emotion and Mood Recognition #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining #Speech Recognition and Synthesis
paper · pdf · doi:10.48550/arxiv.1801.06353
openalex publication_date 2018/01/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The majority of existing speech emotion recognition research focuses on automatic emotion detection using training and testing data from same corpus collected under the same conditions. The performance of such systems has been shown to drop significantly in cross-corpus and cross-language scenarios. To address the problem, this paper exploits a transfer learning technique to improve the performance of speech emotion recognition systems that is novel in cross-language and cross-corpus scenarios. Evaluations on five different corpora in three different languages show that Deep Belief Networks (DBNs) offer better accuracy than previous approaches on cross-corpus emotion recognition, relative to a Sparse Autoencoder and SVM baseline system. Results also suggest that using a large number of languages for training and using a small fraction of the target data in training can significantly boost accuracy compared with baseline also for the corpus with limited training examples.