Recognition of emotional states in natural speech

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

doi:10.1109/sps.2013.6623599

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…In 2009, at the Affective Computing and Intelligent Interaction (ACII) conference, a session was held dedicated to emotion recognition from ambiguous samples (containing a mixture of emotions). This started a new wave in the field of emotion recognition in which the researchers abandoned acted-out speech in favor of spontaneous speech [6]. In line with that, this article describes a database of Polish emotional speech extracted from natural discussions in TV programs.…”

Section: Introductionmentioning

confidence: 89%

Efficiency of chosen speech descriptors in relation to emotion recognition

Kamińska¹,

Sapiński²,

Anbarjafari

2017

J AUDIO SPEECH MUSIC PROC.

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This research paper presents parametrization of emotional speech using a pool of common features utilized in emotion recognition such as fundamental frequency, formants, energy, MFCC, PLP, and LPC coefficients. The pool is additionally expanded by perceptual coefficients such as BFCC, HFCC, RPLP, and RASTA PLP, which are used in speech recognition, but not applied in emotion detection. The main contribution of this work is the comparison of the accuracy performance of emotion detection for each feature type based on the results provided by both k-NN and SVM algorithms with 10-fold cross-validation. Analysis was performed on two different Polish emotional speech databases: voice performances by professional actors in comparison with the author's spontaneous speech.

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Section: Introductionmentioning

confidence: 89%

Efficiency of chosen speech descriptors in relation to emotion recognition

Kamińska¹,

Sapiński²,

Anbarjafari

2017

J AUDIO SPEECH MUSIC PROC.

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“…Contemporarily, two most popular models exist in the literature: JamesLange's and Plutchik's models [3,4,6]. The first one assumes that behavioural and somatic changes are interpreted as emotion which triggers a reaction.…”

Section: Analysis Of Issuesmentioning

confidence: 99%

Kohonen network as a classifier of Polish emotional speech

Powroźnik

2017

ITM Web Conf.

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Abstract. The power of speech is a main tool in human communication. There are a lot of factors as age, emotions, gender, pitch of the voice which can influence features of speech. Obviously, information conveyed by voice intonation has more than only textual meaning. The same sentence pronounced in two different ways can have two completely different meanings. This paper describes Kohonen networks as a classifier of Polish emotional speech. The usage of Discrete Wavelet Transform (DWT) as well as an innovative approach to scaleogram processing is also presented in this article. Mexican Hat Wavelet and the Haar Wavelet were used in researches. All simulations were carried out in MatLab 2016 with Neural Network Toolbar. During whole research more than 9000 simulation have been done. Three different speech databases were used in conducted researches. One of them was prepared by professional actorsfour women and four men, and contains 240 wav files. Two others are results of researchers works. The structures of used Kohonen networks depend on speech signal decomposition's level and scaleogram division. During conducted researches the following emotional states were considered: anger, joy, sadness, boredom, fear and neutral state. Achieved results were between 68% and 80% depends of used wavelet, speech signal and signal decomposition's level.

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Human Emotion: A Survey focusing on Languages, Ontologies, Datasets, and Systems

2022

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Emotions are an essential part of a person’s mental state and influence her/his behavior accordingly. Consequently, emotion recognition and assessment can play an important role in supporting people with ambient assistance systems or clinical treatments. Automation of human emotion recognition and emotion-aware recommender systems are therefore increasingly being researched. In this paper, we first consider the essential aspects of human emotional functioning from the perspective of cognitive psychology and, based on this, we analyze the state of the art in the whole field of work and research to which automated emotion recognition belongs. In this way, we want to complement the already published surveys, which usually refer to only one aspect, with an overall overview of the languages ontologies, datasets, and systems/interfaces to be found in this area. We briefly introduce each of these subsections and discuss related approaches regarding methodology, technology, and publicly accessible artefacts. This comes with an update to recent findings that could not yet be taken into account in previous surveys. The paper is based on an extensive literature search and analysis, in which we also made a particular effort to locate relevant surveys and reviews. The paper closes with a summary of the results and an outlook on open research questions.

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Recognition of emotional states in natural speech

Cited by 4 publications

References 19 publications

Efficiency of chosen speech descriptors in relation to emotion recognition

Efficiency of chosen speech descriptors in relation to emotion recognition

Kohonen network as a classifier of Polish emotional speech

Human Emotion: A Survey focusing on Languages, Ontologies, Datasets, and Systems

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