Emotion, Affect and Personality in Speech: The Bias of by Swati Johar

By Swati Johar

This publication explores some of the different types of speech edition and works to attract a line among linguistic and paralinguistic phenomenon of speech.  Paralinguistic distinction is important to human speech yet has confirmed to be the most tough projects in speech platforms. within the quest for options to speech expertise and sciences, this e-book narrows down the distance among speech technologists and phoneticians and emphasizes the critical efforts required to complete the objective of paralinguistic regulate in speech know-how functions and the extreme want for a multidisciplinary categorization system. This interdisciplinary paintings on paralanguage won't in simple terms function a resource of data but in addition a theoretical version for linguists, sociologists, psychologists, phoneticians and speech researchers.

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Extra resources for Emotion, Affect and Personality in Speech: The Bias of Language and Paralanguage (SpringerBriefs in Electrical and Computer Engineering)

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Gong Y (1995) Speech recognition in noisy environments: a survey. Holzman TG (1999) Computer-human interface solutions for emergency medical care. Oviatt SL, Cohen PR, Wu L, Vergo J, Duncan L, Suhm B, Bers J, Holzman T, Winograd T, Landay J, Larson J, Ferro D (2000) Designing the user interface for multimodal speech and gesture applications: state-of-the-art systems and research directions. Huang X, Acero A, Chelba C, Deng L, Duchene D, Goodman J, Hon H, Jacoby D, Jiang L, Loynd R, Mahajan M, Mau P, Meredith S, Mughal S, Neto S, Plumpe M, Wang K, Wang Y (2000) MiPad: a next-generation PDA prototype.

However, several applications exist where it is beneficial for computers to recognize human emotions. Stress monitoring, online tutoring and diagnosis of psychological disorders are prospective areas where a computer’s functionality may be enhanced to be more aware of the human user’s emotional and attentional expressions. Psychology and engineering communities are working towards development of automatic ways to analyze gestures, facial expressions, vocal emotions and physiological signals to understand and characterize emotions as a goal towards achieving human-computer intelligent interaction.

Current HMMs focus on the linguistic information and remove most of the paralinguistic information from the speech signal. As discussed previously and shown by speech perception experiments, paralinguistic information plays a crucial role in human speech perception. Morgan et al. [10] discusses a parametric and structure based approach which overcomes the mentioned limitation and exploits the knowledge and mechanisms of human speech perception and production by taking into account the relationship between speaking rate variations and the corresponding changes in the acoustic features [9].

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