Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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.

Journal reference: Appl. Sci. 2021, 11, 9897
DOI: 10.3390/app11219897

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

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.