Statistical Evaluation of Speech Features for Emotion Recognition

Iliou, Theodoros; Anagnostopoulos, Christos‐Nikolaos

doi:10.1109/icdt.2009.30

Cited by 31 publications

(13 citation statements)

References 15 publications

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“…However, emotion recognition is a challenging task, even for humans [13] [33], as is verified by the related literature [8] [20] [25]. This can be attributed to a multitude of reasons.…”

Section: Introductionmentioning

confidence: 64%

“…According to [39], for the feature of pitch range it is true that anger, happiness, and fear have a much wide pitch range, sadness has a slightly narrower, whereas disgust has a slightly wide one. Both pitch and energy for happiness and anger are usually higher than sadness [25]. Fear and anger present a high energy lever, whereas sadness demonstrates a low one [50].…”

Section: Feature Extractionmentioning

confidence: 99%

“…On the other hand, several features are investigated here for the first time for the emotion recognition task. Mean of the 1st, 2nd, 3rd, and 4th formant [5][6][7][8] Maximum of the 1st, 2nd, 3rd, and 4th formant [9][10][11][12] Minimum of the 1st, 2nd, 3rd, and 4th formant [13][14][15][16] Variance of the 1st, 2nd, 3rd, and 4th formant [17][18][19][20] Skewness of the 1st, 2nd, 3rd, and 4th formant [21][22][23][24] Interquartile range of the 1st, 2nd, 3rd, and 4th formant [25][26][27][28] Range (i.e. maximum-minimum) of the 1st, 2nd, 3rd, and 4th formant [29][30][31][32] 90th percentile of the 1st, 2nd, 3rd, and 4th formant Several observations are made by the research community with respect to the correspondence among features and emotional categories.…”

Section: Feature Extractionmentioning

confidence: 99%

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Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

Kotti

Paternò

2012

Int J Speech Technol

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In this paper, a psychologically-inspired binary cascade classification schema is proposed for speech emotion recognition. Performance is enhanced because commonly confused pairs of emotions are distinguishable from one another. Extracted features are related to statistics of pitch, formants, and energy contours, as well as spectrum, cepstrum, perceptual and temporal features, autocorrelation, MPEG-7 descriptors, Fujisaki's model parameters, voice quality, jitter, and shimmer. Selected features are fed as input to K nearest neighborhood classifier and to support vector machines. Two kernels are tested for the latter: linear and Gaussian radial basis function. The recently proposed speaker-independent experimental protocol is tested on the Berlin emotional speech database for each gender separately. The best emotion recognition accuracy, achieved by support vector machines with linear kernel, equals 87.7%, outperforming state-of-the-art approaches. Statistical analysis is first carried out with respect to the classifiers' error rates and then to evaluate the information expressed by the classifiers' confusion matrices.

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“…However, emotion recognition is a challenging task, even for humans [13] [33], as is verified by the related literature [8] [20] [25]. This can be attributed to a multitude of reasons.…”

Section: Introductionmentioning

confidence: 64%

Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

Kotti

Paternò

2012

Int J Speech Technol

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“…The authors calculate features and then apply algorithm to select most relevant features and show that prosodic features are more helpful in detection of emotion than spectral features. Another framework in which they extracted 133 speech features and aimed to identify feature set that would be appropriate to descriminate between seven emotions based on speech processing [7]. They use Neural network classifier with 35 input vectors and tested their model using Berlin dataset that include speaker dependant and speaker independent instances.…”

Section: Resultsmentioning

confidence: 99%

Emotion Recognition from Speech using Prosodic and Linguistic Features

Pervaiz¹,

Khan²

2016

ijacsa

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Abstract-Speech signal can be used to extract emotions. However, it is pertinent to note that variability in speech signal can make emotion extraction a challenging task. There are a number of factors that indicate presence of emotions. Prosodic and temporal features have been used previously for the purpose of identifying emotions. Separately, prosodic/temporal and linguistic features of speech do not provide results with adequate accuracy. We can also find out emotions from linguistic features if we can identify contents. Therefore, We consider prosodic as well as temporal or linguistic features which help increasing accuracy of emotion recognition, which is our first contribution reported in this paper. We propose a two-step model for emotion recognition; we extract emotions based on prosodic features in the first step. We extract emotions from word segmentation combined with linguistic features in the second step. While performing our experiments, we prove that the classification mechanisms, if trained without considering age factor, do not help improving accuracy. We argue that the classifier should be based on the age group on which the actual emotion extraction be required, and this becomes our second contribution submitted in this paper.

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“…Numerous acoustic features, such as prosodic features [4][5][6], spectral features [7][8][9], and voice quality [10,11], are applied to emotion recognition. Some emotions present similarities; thus, using only one type of acoustic feature to recognize emotions is inadequate.…”

Section: Introductionmentioning

confidence: 99%

A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition

Zhang

Sun

2017

Mathematical Problems in Engineering

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Selecting an appropriate recognition method is crucial in speech emotion recognition applications. However, the current methods do not consider the relationship between emotions. Thus, in this study, a speech emotion recognition system based on the fuzzy cognitive map (FCM) approach is constructed. Moreover, a new FCM learning algorithm for speech emotion recognition is proposed. This algorithm includes the use of the pleasure-arousal-dominance emotion scale to calculate the weights between emotions and certain mathematical derivations to determine the network structure. The proposed algorithm can handle a large number of concepts, whereas a typical FCM can handle only relatively simple networks (maps). Different acoustic features, including fundamental speech features and a new spectral feature, are extracted to evaluate the performance of the proposed method. Three experiments are conducted in this paper, namely, single feature experiment, feature combination experiment, and comparison between the proposed algorithm and typical networks. All experiments are performed on TYUT2.0 and EMO-DB databases. Results of the feature combination experiments show that the recognition rates of the combination features are 10%-20% better than those of single features. The proposed FCM learning algorithm generates 5%-20% performance improvement compared with traditional classification networks.

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Statistical Evaluation of Speech Features for Emotion Recognition

Cited by 31 publications

References 15 publications

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

Speaker-independent emotion recognition exploiting a psychologically-inspired binary cascade classification schema

Emotion Recognition from Speech using Prosodic and Linguistic Features

A New Fuzzy Cognitive Map Learning Algorithm for Speech Emotion Recognition

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