Mental Illness Disorder Diagnosis Using Emotion Variation Detection from Continuous English Speech

作     者：S.Lalitha Deepa Gupta Mohammed Zakariah Yousef Ajami Alotaibi 

作者机构：Department of Electronics&Communication EngineeringAmrita School of EngineeringAmrita Vishwa VidyapeethamBengaluruIndia Department of Computer Science&EngineeringAmrita School of EngineeringAmrita Vishwa VidyapeethamBengaluruIndia Department of Computer EngineeringCollege of Computer and Information SciencesKing Saud UniversitySaudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第12期

页      面：3217-3238页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：This work was partially supported by the Research Groups Program(Research Group Number RG-1439-033) under the Deanship of Scientific Research King Saud University Riyadh Saudi Arabia 

主　　题：Continuous speech cepstral bi-spectral multi-emotional discrete emotion filter bank mental illness 

摘      要：Automatic recognition of human emotions in a continuous dialog model remains challenging where a speaker’s utterance includes several sentences that may not always carry a single *** work with standalone speech emotion recognition(SER)systems proposed for continuous speech only has been *** the recent decade,various effective SER systems have been proposed for discrete speech,i.e.,short speech *** would be more helpful if these systems could also recognize emotions from continuous ***,if these systems are applied directly to test emotions from continuous speech,emotion recognition performance would not be similar to that achieved for discrete speech due to the mismatch between training data(from training speech)and testing data(from continuous speech).The problem may possibly be resolved if an existing SER system for discrete speech is ***,in this work the author’s existing effective SER system for multilingual and mixed-lingual discrete speech is enhanced by enriching the cepstral speech feature set with bi-spectral speech features and a unique functional set of Mel frequency cepstral coefficient features derived from a sine filter *** augmentation is applied to combat skewness of the SER system toward certain *** using random forest is *** enhanced SER system is used to predict emotions from continuous speech with a uniform segmentation *** to data scarcity,several audio samples of discrete speech from the SAVEE database that has recordings in a universal language,i.e.,English,are concatenated resulting in multi-emotional speech ***,fear,sad,and neutral emotions,which are vital during the initial investigation of mentally disordered individuals,are selected to build six categories of multi-emotional *** results demonstrate the suitability of the proposed method for recognizing emotions from continuous speech as well as from discrete speech.