Bidirectional parallel echo state network for speech emotion recognition
Document Type
Article
Publication Date
10-1-2022
Abstract
Speech is an effective way for communicating and exchanging complex information between humans. Speech signal has involved a great attention in human-computer interaction. Therefore, emotion recognition from speech has become a hot research topic in the field of interacting machines with humans. In this paper, we proposed a novel speech emotion recognition system by adopting multivariate time series handcrafted feature representation from speech signals. Bidirectional echo state network with two parallel reservoir layers has been applied to capture additional independent information. The parallel reservoirs produce multiple representations for each direction from the bidirectional data with two stages of concatenation. The sparse random projection approach has been adopted to reduce the high-dimensional sparse output for each direction separately from both reservoirs. Random over-sampling and random under-sampling methods are used to overcome the imbalanced nature of the used speech emotion datasets. The performance of the proposed parallel ESN model is evaluated from the speaker-independent experiments on EMO-DB, SAVEE, RAVDESS, and FAU Aibo datasets. The results show that the proposed SER model is superior to the single reservoir and the state-of-the-art studies.
Keywords
Speech emotion recognition, Reservoir computing, Random resampling, Recurrent neural network
Divisions
fsktm
Funders
COVID-19 Special Research Grant [CSRG008-2020ST],Universiti Malaya [IIRG002C-19HWB],AUA-UAEU Joint Research Grant [31R188]
Publication Title
Neural Computing & Applications
Volume
34
Issue
20
Publisher
Springer London Ltd
Publisher Location
236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND