Performance Analysis of a Chunk-Based Speech Emotion Recognition Model Using RNN  

作　　者：Hyun-Sam Shin Jun-Ki Hong 

机构地区：[1]Division of Software Convergence,Hanshin University,Osan-si,18101,Korea [2]Division of AI Software Engineering,Pai Chai University,Daejeon,35345,Korea

出　　处：《Intelligent Automation & Soft Computing》2023年第4期235-248,共14页智能自动化与软计算（英文）

基　　金：supported by the“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-004).

摘　　要：Recently,artificial-intelligence-based automatic customer response sys-tem has been widely used instead of customer service representatives.Therefore,it is important for automatic customer service to promptly recognize emotions in a customer’s voice to provide the appropriate service accordingly.Therefore,we analyzed the performance of the emotion recognition(ER)accuracy as a function of the simulation time using the proposed chunk-based speech ER(CSER)model.The proposed CSER model divides voice signals into 3-s long chunks to effi-ciently recognize characteristically inherent emotions in the customer’s voice.We evaluated the performance of the ER of voice signal chunks by applying four RNN techniques—long short-term memory(LSTM),bidirectional-LSTM,gated recurrent units(GRU),and bidirectional-GRU—to the proposed CSER model individually to assess its ER accuracy and time efficiency.The results reveal that GRU shows the best time efficiency in recognizing emotions from speech signals in terms of accuracy as a function of simulation time.

关 键 词：RNN speech emotion recognition attention mechanism time efficiency 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]