Comparison of perceptual features efficiency for automatic identification of emotional states from speech

Kamińska, Dorota; Sapiński, Tomasz; Pelikant, A.

doi:10.1109/hsi.2013.6577824

Cited by 12 publications

(4 citation statements)

References 26 publications

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“…First, we extracted the most popular and most used acoustic features in the SER field, which include the MFCC, Energy, and their first-order derivatives, using open-source media interpretation by large feature-space extraction (openSMILE) [51]. Several studies have used MFCC acoustic features for speech emotion recognition [52][53][54]. We also considered those acoustic features as a baseline to evaluate our acoustic features.…”

Section: Acoustic Feature Extraction and Selectionmentioning

confidence: 99%

Discriminating Emotions in the Valence Dimension from Speech Using Timbre Features

2019

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The most used and well-known acoustic features of a speech signal, the Mel frequency cepstral coefficients (MFCC), cannot characterize emotions in speech sufficiently when a classification is performed to classify both discrete emotions (i.e., anger, happiness, sadness, and neutral) and emotions in valence dimension (positive and negative). The main reason for this is that some of the discrete emotions, such as anger and happiness, share similar acoustic features in the arousal dimension (high and low) but are different in the valence dimension. Timbre is a sound quality that can discriminate between two sounds even with the same pitch and loudness. In this paper, we analyzed timbre acoustic features to improve the classification performance of discrete emotions as well as emotions in the valence dimension. Sequential forward selection (SFS) was used to find the most relevant acoustic features among timbre acoustic features. The experiments were carried out on the Berlin Emotional Speech Database and the Interactive Emotional Dyadic Motion Capture Database. Support vector machine (SVM) and long short-term memory recurrent neural network (LSTM-RNN) were used to classify emotions. The significant classification performance improvements were achieved using a combination of baseline and the most relevant timbre acoustic features, which were found by applying SFS on a classification of emotions for the Berlin Emotional Speech Database. From extensive experiments, it was found that timbre acoustic features could characterize emotions sufficiently in a speech in the valence dimension.

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Section: Acoustic Feature Extraction and Selectionmentioning

confidence: 99%

Discriminating Emotions in the Valence Dimension from Speech Using Timbre Features

2019

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“…In this paper Mel Frequency Cepstral Coefficients (MFCC), Bark Frequency Cepstral Coefficients (BFCC), Perceptual Linear Prediction Coefficients (PLP) and Revised Perceptual Linear Prediction Coefficients (RPLP), were taken into consideration. Perceptual approach in emotion recognition is presented in [25].…”

Section: Speech Signal Parametersmentioning

confidence: 99%

Polish Emotional Speech Recognition Based on the Committee of Classifiers

Kamińska

Sapiński

2017

ELECTROTECHNICAL REVIEW

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This article presents the novel method for emotion recognition from polish speech. We compared two different databases: spontaneous and acted out speech. For the purpose of this research we gathered a set of audio samples with emotional information, which serve as input database. Multiple Classifier Systems were used for classification, with commonly used speech descriptors and different groups of perceptual coefficients as features extracted from audio samples.. Streszczenie. Niniejsza praca dotyczy rozpoznawania stanów emocjonalnych na podstawie głosu. W artykule porównaliśmy mowę spontaniczną z mową odegraną. Na potrzeby zrealizowanych badań zgromadzone zostały emocjonalne nagrania audio, stanowiące kompleksową bazę wejściową. Przedstawiamy nowatorski sposób klasyfikacji emocji wykorzystujący komitety klasyfikujące, stosując do opisu emocji powszechnie używane deskryptory sygnału mowy oraz percepcyjne współczynniki hybrydowe. (Rozpoznawanie emocji na podstawie sygnału mowy przy użyciu komitetów klasyfikujących)

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“…Cepstral Coefficients (BFCC) and Mel-frequency Cepstral Coefficients (MFCC) [15] with a clean dataset. Hidden Markov Model (HMM) [11] is implemented using Hidden Markov Toolkit (HTK) [12].…”

Section: Revised Perceptual Linear Prediction (Rplp) Bark Frequencymentioning

confidence: 99%

Automatic Speech Recognition System: A Survey Report

Singh¹

2016

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Comparison of perceptual features efficiency for automatic identification of emotional states from speech

Cited by 12 publications

References 26 publications

Discriminating Emotions in the Valence Dimension from Speech Using Timbre Features

Discriminating Emotions in the Valence Dimension from Speech Using Timbre Features

Polish Emotional Speech Recognition Based on the Committee of Classifiers

Automatic Speech Recognition System: A Survey Report

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