Emotion recognition from speech: WOC-NN and class-interaction

Attabi, Yazid; Dumouchel, Pierre

doi:10.1109/isspa.2012.6310487

Cited by 14 publications

(3 citation statements)

References 5 publications

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“…Recognition of feature vectors is generally performed using well known algorithms, starting from vector classification methods, such as Support Vector Machines [11] [12], various types of Neural Networks [13] [14], different types of the k-NN algorithm [15] [16] or using hidden Markov model (HMM) and its variations [17]. Some scientists create multimodal or hierarchical classifiers by combining existing methods in order to improve recognition results [18].…”

Section: Related Workmentioning

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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Section: Related Workmentioning

confidence: 99%

Polish Emotional Speech Recognition Based on the Committee of Classifiers

Kamińska

Sapiński

2017

ELECTROTECHNICAL REVIEW

View full text Add to dashboard Cite

show abstract

“…Recognition of feature vectors is generally performed using well known algorithms, starting from vector classification methods, such as Support Vector Machines [11] [12], various types of Neural Networks [13] [14], different types of the k-NN algorithm [15] [16] or using graphical models such as hidden Markov model (HMM) and its variations [17]. Some scientists create multimodal or hierarchical classifiers by combining existing methods in order to improve recognition results [18].…”

Section: Related Workmentioning

confidence: 99%

Recognition of emotional states in natural speech

Kamińska

Sapiński

Pelikant

2013

2013 Signal Processing Symposium (SPS)

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Research in emotional speech recognition is generally focused on analysis of a set of primary emotions. However, it is clear that spontaneous speech, which is more intricate comparing to acted out utterances, carries information about emotional complexity or degree of their intensity. This research refers to the theory of Robert Plutchik, who suggested the existence of eight primary emotions. All other states are derivatives and occur as combinations, mixtures or compounds of the primary emotions. In this paper authors presents a novel approach for automatic speech clustering into subgroups representing primary emotions intensities. For this purpose a multimodal classifier based on k-means algorithm was implemented and tested on Polish spontaneous speech database. Studies have been conducted using prosodic features and perceptual coefficients. Results have shown that the proposed measure is effective in recognition of intensity of the predicted emotion.

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“…Recognition of feature vectors is generally performed using well known algorithms, starting from vector classification methods, such as Support Vector Machines [18,19], various types of Neural Networks [2,10], different types of the k-NN algorithm [1,20] or using graphical models such as hidden Markov model (HMM) and its variations [12]. Some scientists create multimodal or hierarchical classifiers by combining existing methods in order to improve recognition results [17].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Emotion Intensity Basing on Neutral Speech Model

Kamińska¹,

Sapiński²,

Pelikant³

2014

Advances in Intelligent Systems and Computing

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Abstract. Research in emotional speech recognition is generally focused on analysis of a set of primary emotions. However it is clear that spontaneous speech, which is more intricate comparing to acted out utterances, carries information about emotional complexity or degree of their intensity. This paper refers to the theory of Robert Plutchik, who suggested the existence of eight primary emotions. All other states are derivatives and occur as combinations, mixtures or compounds of the primary emotions. During the analysis Polish spontaneous speech database containing manually created confidence labels was implemented as a training and testing set. Classification results of four primary emotions (anger, fear, joy, sadness) and their intensities have been presented. The level of intensity is determined basing on the similarity of particular emotion to neutral speech. Studies have been conducted using prosodic features and perceptual coefficients. Results have shown that the proposed measure is effective in recognition of intensity of the predicted emotion.

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Emotion recognition from speech: WOC-NN and class-interaction

Cited by 14 publications

References 5 publications

Polish Emotional Speech Recognition Based on the Committee of Classifiers

Polish Emotional Speech Recognition Based on the Committee of Classifiers

Recognition of emotional states in natural speech

Recognition of Emotion Intensity Basing on Neutral Speech Model

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