Editing Affective computing (section)

====Algorithms====

The process of speech/text affect detection requires the creation of a reliable [[database]], [[knowledge base]], or [[vector space model]],<ref name = "Osgood75">
{{cite book
 | author = Charles Osgood
 |author2=William May|author3=Murray Miron
 | title = Cross-Cultural Universals of Affective Meaning
 | url = https://archive.org/details/crossculturaluni00osgo
 | url-access = registration
 | publisher = Univ. of Illinois Press
 | isbn = 978-94-007-5069-2
 | year = 1975
}}
</ref> broad enough to fit every need for its application, as well as the selection of a successful classifier which will allow for quick and accurate emotion identification.

{{Asof|2010}}, the most frequently used classifiers were linear discriminant classifiers (LDC), k-nearest neighbor (k-NN), Gaussian mixture model (GMM), support vector machines (SVM), artificial neural networks (ANN), decision tree algorithms and hidden Markov models (HMMs).<ref name="Scherer-2010-p241">{{harvnb|Scherer|Bänziger|Roesch|2010|p=241}}</ref> Various studies showed that choosing the appropriate classifier can significantly enhance the overall performance of the system.<ref name="Hudlicka-2003-p24"/> The list below gives a brief description of each algorithm:

* [[Linear classifier|LDC]] – Classification happens based on the value obtained from the linear combination of the feature values, which are usually provided in the form of vector features.
* [[K-nearest neighbor algorithm|k-NN]] – Classification happens by locating the object in the feature space, and comparing it with the k nearest neighbors (training examples). The majority vote decides on the classification.
* [[Gaussian mixture model|GMM]] – is a probabilistic model used for representing the existence of subpopulations within the overall population. Each sub-population is described using the mixture distribution, which allows for classification of observations into the sub-populations.<ref>[http://cnx.org/content/m13205/latest/ "Gaussian Mixture Model"]. Connexions – Sharing Knowledge and Building Communities. Retrieved 10 March 2011.</ref>
* [[Support vector machine|SVM]] – is a type of (usually binary) linear classifier which decides in which of the two (or more) possible classes, each input may fall into.
* [[Artificial neural network|ANN]] – is a mathematical model, inspired by biological neural networks, that can better grasp possible non-linearities of the feature space.
* [[Decision tree learning|Decision tree algorithms]] – work based on following a decision tree in which leaves represent the classification outcome, and branches represent the conjunction of subsequent features that lead to the classification.
* [[Hidden Markov model|HMMs]] – a statistical Markov model in which the states and state transitions are not directly available to observation. Instead, the series of outputs dependent on the states are visible. In the case of affect recognition, the outputs represent the sequence of speech feature vectors, which allow the deduction of states' sequences through which the model progressed. The states can consist of various intermediate steps in the expression of an emotion, and each of them has a probability distribution over the possible output vectors. The states' sequences allow us to predict the affective state which we are trying to classify, and this is one of the most commonly used techniques within the area of speech affect detection.

It is proved that having enough acoustic evidence available the emotional state of a person can be classified by a set of majority voting classifiers. The proposed set of classifiers is based on three main classifiers: kNN, C4.5 and SVM-RBF Kernel. This set achieves better performance than each basic classifier taken separately. It is compared with two other sets of classifiers: one-against-all (OAA) multiclass SVM with Hybrid kernels and the set of classifiers which consists of the following two basic classifiers: C5.0 and Neural Network. The proposed variant achieves better performance than the other two sets of classifiers.<ref>{{cite journal|url=http://ntv.ifmo.ru/en/article/11200/raspoznavanie_i_prognozirovanie_dlitelnyh__emociy_v_rechi_(na_angl._yazyke).htm|title=Extended speech emotion recognition and prediction|author=S.E. Khoruzhnikov|journal=Scientific and Technical Journal of Information Technologies, Mechanics and Optics|volume=14|issue=6|page=137|year=2014|display-authors=etal}}</ref>