9 resultados para emotional speech


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The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

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Accurate and fast decoding of speech imagery from electroencephalographic (EEG) data could serve as a basis for a new generation of brain computer interfaces (BCIs), more portable and easier to use. However, decoding of speech imagery from EEG is a hard problem due to many factors. In this paper we focus on the analysis of the classification step of speech imagery decoding for a three-class vowel speech imagery recognition problem. We empirically show that different classification subtasks may require different classifiers for accurately decoding and obtain a classification accuracy that improves the best results previously published. We further investigate the relationship between the classifiers and different sets of features selected by the common spatial patterns method. Our results indicate that further improvement on BCIs based on speech imagery could be achieved by carefully selecting an appropriate combination of classifiers for the subtasks involved.

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Feature-based vocoders, e.g., STRAIGHT, offer a way to manipulate the perceived characteristics of the speech signal in speech transformation and synthesis. For the harmonic model, which provide excellent perceived quality, features for the amplitude parameters already exist (e.g., Line Spectral Frequencies (LSF), Mel-Frequency Cepstral Coefficients (MFCC)). However, because of the wrapping of the phase parameters, phase features are more difficult to design. To randomize the phase of the harmonic model during synthesis, a voicing feature is commonly used, which distinguishes voiced and unvoiced segments. However, voice production allows smooth transitions between voiced/unvoiced states which makes voicing segmentation sometimes tricky to estimate. In this article, two-phase features are suggested to represent the phase of the harmonic model in a uniform way, without voicing decision. The synthesis quality of the resulting vocoder has been evaluated, using subjective listening tests, in the context of resynthesis, pitch scaling, and Hidden Markov Model (HMM)-based synthesis. The experiments show that the suggested signal model is comparable to STRAIGHT or even better in some scenarios. They also reveal some limitations of the harmonic framework itself in the case of high fundamental frequencies.

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Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.

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In recent decades, numerous studies have shown a significant increase in violence during childhood and adolescence. These data suggest the importance of implementing programs to prevent and reduce violent behavior. The study aimed to design a program of emotional intelligence (El) for adolescents and to assess its effects on variables related to violence prevention. The possible differential effect of the program on both genders was also examined. The sample comprised 148 adolescents aged from 13 to 16 years. The study used an experimental design with repeated pretest-posttest measures and control groups. To measure the variables, four assessment instruments were administered before and after the program, as well as in the follow-up phase (1 year after the conclusion of the intervention). The program consisted of 20 one-hour sessions. The pretest-posttest ANCOVAs showed that the program significantly increased: (1) El (attention, clarity, emotional repair); (2) assertive cognitive social interaction strategies; (3) internal control of anger; and (4) the cognitive ability to analyze negative feelings. In the follow-up phase, the positive effects of the intervention were generally maintained and, moreover, the use of aggressive strategies as an interpersonal conflict-resolution technique was significantly reduced. Regarding the effect of the program on both genders, the change was very similar, but the boys increased assertive social interaction strategies, attention, and emotional clarity significantly more than the girls. The importance of implementing programs to promote socio-emotional development and prevent violence is discussed.

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We distinguish two general approaches to inner speech (IS) the "format" and the "activity" views and defend the activity view. The format view grounds the utility of IS on features of the representational format of language, and is related to the thesis that the proper function of IS is to make conscious thinking possible. IS appears typically as a product constituted by representations of phonological features. The view also has implications for the idea that passivity phenomena in cognition may be misat-tributed IS. The activity view sees IS as a speaking activity that does not have a proper function in cognition. It simply inherits the array of functions of outer speech. We argue that it is methodologically advisable to start from this variety of uses, which suggests commonalities between internal and external activities. The format view has several problems; it has to deny "unsymbolized thinking"; it cannot easily explain how IS makes thoughts available to consciousness, and it cannot explain those uses of IS where its format features apparently play no role. The activity view not only lacks these problems but also has explanatory advantages: construing IS as an activity allows it to be integrally constituted by its content; the view is able to construe unsymbolized thinking as part of a continuum of phenomena that exploit the same mechanisms, and it offers a simple explanation for the variety of uses of IS