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It is important to identify a specific speech feature extraction technique in speech emotion recognition that can classify the emotions from speech efficiently. So far, many speech features have been investigated for speech emotion, but the best speech features is not yet discovered. Figure 2 shows some of the examples by categorizing the speech features [1, 2]. However, the combination of speech features to represent the speech signal is the most common practice in speech emotion recognition. In this work, a new TEO-based feature is proposed and is combined with the spectral features. Among these, the SSER system developed using the proposed feature fusion is compared with existing spectral features MFCC, LPCC, and RASTA-PLP-based SSER systems.
The proposed stressed speech emotion recognition (SSER) system is as shown in Figure 3 to classify the stressed emotions such as anger, fear, disgust, sad, and so on, effectively compared to the existing methods. In this proposed system, the speech signal is given to the feature extraction block with the feature fusion of Teager energy-based feature, i.e., TEO-Auto-Env and spectral feature.
Spectral features provided good accuracy so far in emotion classification. But, the drawback of these features is, they treat all the emotions similarly. But, for the stressed emotions, during the speech production process, the energy of the speech signal is deteriorated. Because of this, the complete features of these emotions were not perfectly extracted using the spectral features. Hence, there was a need of a feature to increase the energies of these stressed emotions [31]. A feature based on TEO can be used for this purpose. TEO-Auto-Env is combined with each of the spectral features, i.e., MFCC, LPCC, and RASTA-PLP to extract the features as shown in Figure 6. These combinations of features are given to the classifier to detect the emotions.
Therefore, a new feature based on TEO is proposed in this work, and this feature is combined with the spectral features in order to extract the features of the stressed emotions that were not effectively extracted by spectral features alone. The spectral features considered for the analysis are MFCC, LPCC, and RASTA-PLP.
(4) RASTA-PLP: it uses RASTA filtering in perceptual linear prediction (PLP). Perceptual processing, such as critical band analysis, equal loudness preemphasis, and intensity loudness, is performed before executing autoregressive (AR) modelling. PLP coefficients [33] are created from LP coefficients before performing AR modelling. RASTA filtering [34, 35], also known as bandpass filtering in the log spectral domain, was invented at the same time as PLP. By using this, the slow variations in the channel are suppressed. A general RASTA filter is defined bywhere the numerator is a regression filter of Nth order and denominator is an integrator.
The SSER system developed based on the feature fusion of the TEO-Auto-Env and Spectral features using k-NN classifier provided improved accuracy in case of all the databases compared to the SSER system build using individual spectral features. TEO-Auto-Env feature is based on TEO, as it is basically designed to improve the energies of the stressed emotions. Because of this reason, when TEO-Auto-Env is combined with spectral features, the features that were not able to be extracted from the stressed emotions were extracted, and the combination of the TEO-based feature with spectral features yielded better performance. SSER system is developed for four databases, namely, EMO-DB, EMOVO, IITKGP, and EMA for gender-dependent (GD) and speaker-independent (SI) cases. Among all the combinations, the SSER system developed using the feature fusion of TEO-Auto-Env MFCC and LPCC gave the highest accuracy in the classification of stressed emotions with 91.4% (SI), 91.4% (GD-male), and 93.1% (GD-female) for EMO-DB; 68.5% (SI), 68.5% (GD-male), and 74.6% (GD-female) for EMOVO; 90.6%(SI), 91% (GD-male), and 92.3% (GD-female) for EMA; and 95.1% (GD-female) for IITKGP female database compared to other feature fusions, which shows a favorable recognition performance in independent emotion speech recognition experiment. Also, the classification accuracy of the SSER system with the proposed feature showed higher accuracy compared to the features discussed in the literature.
The objective of speech emotion recognition (SER) is to enhance man-machine interface. It can also be used to cover the physiological state of a person in critical situations. In recent time, speech emotion recognition also finds its operations in medicine and forensics. A new feature extraction technique using Teager energy operator (TEO) is proposed for the detection of stressed emotions as Teager energy-autocorrelation envelope (TEO-Auto-Env). TEO is basically designed for increasing the energies of the stressed speech signals whose energies are reduced during the speech production process and hence used in this analysis. A stressed speech emotion recognition (SSER) system is developed using TEO-Auto-Env and spectral feature combination for detecting the emotions. The spectral features considered are Mel-frequency cepstral coefficients (MFCC), linear prediction cepstral coefficients (LPCC), and relative spectra-perceptual linear prediction (RASTA-PLP). EMO-DB (German), EMOVO (Italian), IITKGP (Telugu), and EMA (English) databases are used in this analysis. The classification of the emotions is carried out using the k-nearest neighborhood (k-NN) classifier for gender-dependent (GD) and speaker-independent (SI) cases. The proposed SSER system provides improved accuracy compared to the existing ones. Average recall is used for performance evaluation. The highest classification accuracy is achieved using the feature combination of TEO-Auto-Env, MFCC, and LPCC features with 91.4% (SI), 91.4% (GD-male), and 93.1%(GD-female) for EMO-DB; 68.5% (SI), 68.5% (GD-male), and 74.6% (GD-female) for EMOVO; 90.6%(SI), 91% (GD-male), and 92.3% (GD-female) for EMA; and 95.1% (GD-female) for IITKGP female database.
The distribution of radiation can be divided into two regions. Abovea wavelength of around 1000 Angstroms (solid curve) the radiation producedby the Sun is 'thermal' in origin - i.e. it arises because the Sun is a hot object. The spectral distribution has ashape known as a black body curve with the peak occurring at around 5000 Angstroms - in the middle of the range ofwavelengths which we are able to see. For higher, or lower, wavelengths the radiation produced by the Sundecreases.
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486 nm","author":"@type":"Person","name":"Infinity Learn","upvoteCount":"0","url":"https:\/\/infinitylearn.com\/surge\/question\/chemistry\/the-wavelength-of-the-spectral-line-when-the-electron-in-the\/"},"text":"The wavelength of the spectral line when the electron in the hydrogen atom undergoes a transition from the energy level 4 to energy level 2 is","author":"@type":"Organization","name":"Infinity Learn by Sri Chaitanya"}}var ewww_webp_supported=false;Skip to contentUse AppSTUDY MATERIALSNCERT SolutionsClass 12MathsPhysicsChemistryBiologyClass 11MathsPhysicsChemistryBiologyClass 10MathsScienceSocial ScienceEnglishClass 9MathsScienceSocial ScienceEnglishClass 8MathsScienceSocial ScienceEnglishClass 7MathsScienceSocial ScienceEnglishClass 6MathsScienceSocial ScienceEnglishNCERT Exemplar SolutionsClass 12MathsPhysicsChemistryBiologyClass 11MathsPhysicsChemistryBiologyClass 10MathsScienceClass 9MathsScienceClass 8MathsScienceClass 7MathsScienceClass 6MathsScienceCOURSESSCORE Scholarship ExamJEEGrade 11Grade 12Grade 13NEETGrade 11Grade 12Grade 13FoundationGrade 8Grade 9Grade 10CBSEPhysicsPhysics Important QuestionsChemistryChemistry Important QuestionsMathsMaths Important QuestionsBiologyBiology Important QuestionsEnglishEssaysEnglish Chapter SummariesMoreFull FormBlogIIT-JEENEETCBSENCERTScholarshipGeneral
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