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A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition
Version 1
: Received: 20 August 2021 / Approved: 23 August 2021 / Online: 23 August 2021 (12:16:40 CEST)
A peer-reviewed article of this Preprint also exists.
Zhang, H.; Huang, H.; Han, H. A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition. Appl. Sci. 2021, 11, 9897. Zhang, H.; Huang, H.; Han, H. A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition. Appl. Sci. 2021, 11, 9897.
Abstract
Speech emotion recognition remains a heavy lifting in natural language processing. It has strict requirements to the effectiveness of feature extraction and that of acoustic model. With that in mind, a Heterogeneous Parallel Convolution Bi-LSTM model is proposed to address these challenges. It consists of two heterogeneous branches: the left one contains two dense layers and a Bi-LSTM layer, while the right one contains a dense layer, a convolution layer, and a Bi-LSTM layer. It can exploit the spatiotemporal information more effectively, and achieves 84.65%, 79.67%, and 56.50% unweighted average recall on the benchmark databases EMODB, CASIA, and SAVEE, respectively. Compared with the previous research results, the proposed model achieves better performance stably.
Keywords
Speech emotion recognition; Feature extraction; Heterogeneous parallel network; Spectral features; Prosodic features; Multi-feature fusion
Subject
Computer Science and Mathematics, Computer Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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