8,740 research outputs found

    Production Methods

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    Vocabulary size influences spontaneous speech in native language users: Validating the use of automatic speech recognition in individual differences research

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    Previous research has shown that vocabulary size affects performance on laboratory word production tasks. Individuals who know many words show faster lexical access and retrieve more words belonging to pre-specified categories than individuals who know fewer words. The present study examined the relationship between receptive vocabulary size and speaking skills as assessed in a natural sentence production task. We asked whether measures derived from spontaneous responses to every-day questions correlate with the size of participantsā€™ vocabulary. Moreover, we assessed the suitability of automatic speech recognition for the analysis of participantsā€™ responses in complex language production data. We found that vocabulary size predicted indices of spontaneous speech: Individuals with a larger vocabulary produced more words and had a higher speech-silence ratio compared to individuals with a smaller vocabulary. Importantly, these relationships were reliably identified using manual and automated transcription methods. Taken together, our results suggest that spontaneous speech elicitation is a useful method to investigate natural language production and that automatic speech recognition can alleviate the burden of labor-intensive speech transcription

    The Influence of Attention to Language Form on the Production of Weak Forms by Polish Learners of English

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    The paper discusses a study whose aim was to examine the impact of attention to language form and task type on the realisation of English function words by Polish learners of English. An additional goal was to investigate whether style-induced pronunciation shifts may depend on the degree of foreign accent. A large part of the paper concentrates on the issue of defining ā€˜weaknessā€™ in English weak forms and considers priorities in English pronunciation teaching as far as the realisation of function words is concerned. The participants in the study were 12 advanced Polish learners of English, who were divided into two groups: 6 who were judged to speak with a slight degree of foreign accent and 6 who were judged to speak with a high degree of foreign accent. The subjectsā€™ pronunciation was analysed in three situations in which we assume their attention was increasingly paid to speech form (spontaneous speech, prepared speech, reading). The results of the study suggest that increased attention to language form caused the participants to realise more function words as unstressed, although the effect was small. It was also found that one of the characteristics of English weak forms, the lack of stress, was realised correctly by the participants in the majority of cases. Finally, the results of the study imply that, in the case under investigation, the effect of attention to language form is weakly or not at all related to the degree of foreign accent

    Affective Medicine: a review of Affective Computing efforts in Medical Informatics

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    Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as ā€œcomputing that relates to, arises from, or deliberately influences emotionsā€. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field

    Towards an artificial therapy assistant: Measuring excessive stress from speech

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    The measurement of (excessive) stress is still a challenging endeavor. Most tools rely on either introspection or expert opinion and are, therefore, often less reliable or a burden on the patient. An objective method could relieve these problems and, consequently, assist diagnostics. Speech was considered an excellent candidate for an objective, unobtrusive measure of emotion. True stress was successfully induced, using two storytelling\ud sessions performed by 25 patients suffering from a stress disorder. When reading either a happy or a sad story, different stress levels were reported using the Subjective Unit of Distress (SUD). A linear regression model consisting of the high-frequency energy, pitch, and zero crossings of the speech signal was able to explain 70% of the variance in the subjectively reported stress. The results demonstrate the feasibility of an objective measurement of stress in speech. As such, the foundation for an Artificial Therapeutic Agent is laid, capable of assisting therapists through an objective measurement of experienced stress

    Speech-based recognition of self-reported and observed emotion in a dimensional space

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    The differences between self-reported and observed emotion have only marginally been investigated in the context of speech-based automatic emotion recognition. We address this issue by comparing self-reported emotion ratings to observed emotion ratings and look at how differences between these two types of ratings affect the development and performance of automatic emotion recognizers developed with these ratings. A dimensional approach to emotion modeling is adopted: the ratings are based on continuous arousal and valence scales. We describe the TNO-Gaming Corpus that contains spontaneous vocal and facial expressions elicited via a multiplayer videogame and that includes emotion annotations obtained via self-report and observation by outside observers. Comparisons show that there are discrepancies between self-reported and observed emotion ratings which are also reflected in the performance of the emotion recognizers developed. Using Support Vector Regression in combination with acoustic and textual features, recognizers of arousal and valence are developed that can predict points in a 2-dimensional arousal-valence space. The results of these recognizers show that the self-reported emotion is much harder to recognize than the observed emotion, and that averaging ratings from multiple observers improves performance
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