Emotion Recognition with Poincare Mapping of Voiced-Speech Segments of Utterances

Emotional Speech Recognition Based on the Committee of Classifiers

“…The Polish Polish Emotional Speech Database (PESD) [ 2 ] was prepared and shared by the Medical Electronics Division, Lodz University of Technology. The database consists of 240 samples recorded in the aula of the Polish National Film Television and Theater School in Lodz.…”

“…Proper identification of emotional state can significantly improve quality of human-computer interfaces. It can be applied for monitoring of psycho-physiological states of individuals e.g., to assess the level of stress or fatigue, forensic data analysis [ 2 ], advertisement [ 3 ], social robotic [ 4 ], video conferencing [ 5 ], violence detection [ 6 ], animation or synthesis of life-like agents xue2018voice, and many others. Automatic emotion recognition methods utilize various input types i.e., facial expressions [ 7 , 8 , 9 ], speech [ 10 , 11 , 12 ], gesture and body language [ 13 , 14 ], physical signals such as electrocardiogram (ECG), electromyography (EMG), electrodermal activity, skin temperature, galvanic resistance, blood volume pulse (BVP), and respiration [ 15 ].…”

“…Current paper is an extension of the previous approach. I investigated three different type of Polish corpora: acted out, in which the actors repeat the same sentence while expressing different emotional states [ 20 ]; acted out, in which the actors repeat several different sentences while expressing different emotional states [ 2 ]; spontaneous speech samples collected from live shows and programs such as reality shows [ 21 ]. I combined different classification methods as nodes (k-NN, MLP, SL, SMO, Bagging, RC, j48, LMT, NBTree, RF) and juxtaposed several alternative approaches to final voting.…”

Persian speech emotion recognition

“…Although this approach requires a short training phase, nevertheless the test phase involves high computational cost. DT is required to high memory and higher error rates occurs if number of samples is low [19].…”

Automatic vocal effort detection for reliable speech recognition

“…Since many speech recognition systems are trained solely on neutrally pronounced speech, they can not efficiently cope with phonetic changes caused by altered psychical or physical state of the speaker. ASR performance relation to various stress conditions and emotional states of a user has received an increasing amount of interest in the last decade [6]- [9]. Other sources of variability may arise from speaker's fatigue level [10], [11], various illnesses, alcohol or drug intoxication, etc.…”