Emotion recognition of the SROL Romanian database using fuzzy KNN algorithm

Zbancioc, Marius Dan; Feraru, Silvia Monica

doi:10.1109/isetc.2012.6408133

Cited by 14 publications

(8 citation statements)

References 6 publications

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“…The research in the project and partly in this study also reflects the interest in detecting attitudes in messages and to a standing interest in detecting emotions in speech and texts as in the studies [9], [13], [25], [28].…”

Section: Introductionmentioning

confidence: 73%

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Language Processes and Related Statistics in the Posts Associated to Disasters on Social Networks

Bolea

2016

INT J COMPUT COMMUN

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This paper provides a detailed and long-period statistics of the use of synonyms in posts related to specific events on social networks (SNs), an extended analysis of the correlations of the flows of the synonyms in such posts, a study of the applicability of Zipf's law to posts related to specific events on SNs, and an analysis of the dynamics of the fluxes of synonyms in the posts. The paper also introduces the study of the distances in the phase space for the characterization of the dynamics of the word fluxes on social networks. This article is a partial report on recent research performed for a deeper analysis of social networks and of processes developing on social networks, including used lexicon, dynamics of messages related to a specific type of topic, and relationships of the processes on SNs with external events.

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Section: Introductionmentioning

confidence: 73%

“…It would be interesting to follow this study by a research of the uttering of the synonyms on voice-enabled SNs in view of detecting their emotional charge using various methods of characterization, such as those in [8], [9], [13], [28] that report on emotion recognition tools specifically for the Romanian language.…”

Section: Discussionmentioning

confidence: 99%

Language Processes and Related Statistics in the Posts Associated to Disasters on Social Networks

Bolea

2016

INT J COMPUT COMMUN

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“…In our previous studies [17,18], we concluded that the jitter and shimmer parameters do not help to the emotion recognition when the parameters are extracted only on the phoneme level. This can be explained by the short duration of the phonemes.…”

Section: Discussionmentioning

confidence: 95%

Speech emotion recognition for SROL database using weighted KNN algorithm

Feraru

Zbancioc

2013

Proceedings of the International Conference on ELECTRONICS, COMPUTERS and ARTIFICIAL INTELLIGENCE - ECAI-2013

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In this study, we utilized an improved version of the classical KNN algorithm which associates to each parameter from the features vectors weights according to their performance in the classification process. We obtained the recognition percents of emotions around 65-67%, for the Romanian language, on the SROL database, which are comparable with the results for other languages, with non-professional voice database. This is the first study when the parameters are extracted on the sentence level. Until now, the analysis was made on the phoneme level.

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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 of the SROL Romanian database using fuzzy KNN algorithm

Cited by 14 publications

References 6 publications

Language Processes and Related Statistics in the Posts Associated to Disasters on Social Networks

Language Processes and Related Statistics in the Posts Associated to Disasters on Social Networks

Speech emotion recognition for SROL database using weighted KNN algorithm

Recognition of Emotion Intensity Basing on Neutral Speech Model

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