A Review on Emotion Recognition Algorithms using Speech Analysis <s> Eager Energy Operator (TEO) <s> A simple algorithm is derived that permits on-the-fly calculation of the energy required to generate, in a certain sense, a signal. The results of applying this algorithm to a number of well-known signals are shown. Some of the invariance and noise properties of the algorithm are derived and verified by simulation. The implementation of the algorithm and its application to speech processing are briefly discussed. > <s> BIB001 </s> A Review on Emotion Recognition Algorithms using Speech Analysis <s> Eager Energy Operator (TEO) <s> The Teager Energy Operator is a nonlinear operator defined for both ::: continuous and discrete time signals. In this thesis we define ::: different versions and extensions of the one-dimensional operator, and ::: then look at the properties and positivity of the operator. Its ::: connection to Volterra systems is explained. The Teager Energy ::: Operator is used for demodulation of AM-FM signals, and we look at how ::: this can be done using the operator and nonlinear methods (Hilbert ::: transform, Prony's, Modified covariance, etc). As the Teager Energy ::: Operator is sensitive to noise, the multiband demodulation technique ::: is introduced to refine the estimates. The performance of linear and ::: nonlinear differentiators is investigated. An application to speech ::: analysis is shown, using a multiband method to locate formants in ::: speech. Finally, a performance comparison in different levels of noise ::: is done. <s> BIB002 </s> A Review on Emotion Recognition Algorithms using Speech Analysis <s> Eager Energy Operator (TEO) <s> Recently, increasing attention has been directed to the study of the emotional content of speech signals, and hence, many systems have been proposed to identify the emotional content of a spoken utterance. This paper is a survey of speech emotion classification addressing three important aspects of the design of a speech emotion recognition system. The first one is the choice of suitable features for speech representation. The second issue is the design of an appropriate classification scheme and the third issue is the proper preparation of an emotional speech database for evaluating system performance. Conclusions about the performance and limitations of current speech emotion recognition systems are discussed in the last section of this survey. This section also suggests possible ways of improving speech emotion recognition systems. <s> BIB003 </s> A Review on Emotion Recognition Algorithms using Speech Analysis <s> Eager Energy Operator (TEO) <s> In this study, Teager-energy based Mel-frequency cepstral coefficients (TEMFCCs) are proposed for Automatic Speech Emotion Recognition (ASER) in noisy environments. TEMFCCs are obtained by taking the absolute value of the Teager-energy operator (TEO) of the short-time Fourier transform of the signal (STFT), warping it to a Mel-frequency scale, and taking the discrete cosine transform (DCT) of the log-Mel Teager-energy spectrum. Experiments on classification of discrete emotion categories show that TEMFCCs are more robust than MFCCs in noisy conditions, while TEMFCCs and MFCCs perform similarly for clean conditions. <s> BIB004
The Teager Energy Operator (TEO) was proposed by Herbert M. Teager and Shushan M. Teager in 1983. In their article, they argued that the speech model at that time was inaccurate due to its linear finite characteristics, and proposed a model that involves a nonlinear process. Later in another article, they generated a plot that implies the energy creating the sound, but the algorithm was not specified BIB002 . The works is further extended in BIB001 and Teager Energy Operator has since been defined for both real and complex continuous signals. TEO can be defined as TEO has been used in various speech signal applications. In BIB002 , formants of vowels are tracked using TEOs. In SER, TEO features are used by BIB004 to make their system more robust in noisy environment. Moreover, TEO-based features are suitable to detect the stress level of emotion BIB003 .