Prosody and iconicity / / edited by Sylvie Hancil, Daniel Hirst

• Record Nr.: UNINA9910812972903321
• Pubbl/distr/stampa: Amsterdam ; ; Philadelphia, : John Benjamins Pub. Co., 2013
• ISBN: 1-299-28377-2; 90-272-7219-0
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (268 p.)
• Collana: Iconicity in language and literature, , 1873-5037 ; ; v. 13
• Altri autori (Persone): HancilSylvie; HirstDaniel
• Disciplina: 414/.6
• Soggetti: Iconicity (Linguistics); Versification; Language and languages
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Prosody and Iconicity; Editorial page; Title page; LCC data; Table of contents; Introduction; References; Prosodic Iconicity and experiential blending; 1. Introduction; 2. The semiotic scene: Overt and global communication models; 2.1 The 'hearer-only perspective'; 2.2 "Abstract information processing"; 2.3 A third model of communication?; 2.4 Prosodies and experience shaping; 2.4.1 Speech rate, rhythm and tempo; 2.4.2 Audible spectrum: Frequencies codes?; 2.4.3 Phonatory posture imitation through formats (proprioceptive formant analyzer) - speech motor imitation; 3. Conceptual blending framework 3.1 Blending; 3.1.1 Perception; 3.1.2 Levels specificity; 3.1.3 Mono- and inter-modal perceptual integration: "Stroop-effect" and McGurck-MacDonald effect; 3.2 Material anchors; 3.2.1 Speaking and writing; 3.2.2 More material anchoring for speaking and writing; 4. Experiential blending; 4.1 The experiential blending; 4.2 Levels of experiential blending; 4.2.1 First level experiential blending; 4.2.2 Second level experiential blending; 4.3 Experiential blending and iconic emergence; 4.3.1 "Experiencing budget" blend; 4.3.2 "Running-talking" experiential blend; 5. Conclusion 6. Annexes; References; Emotional expressions as communicative signals; 1. Introduction; 1.1 Nature of emotion and ; emotional expressions; 1.2 An evolutionary perspective; 1.3 A bio-informational dimensions theory; 2. Preliminary BID interpretation of existing data; 2.1 Anger/happiness; 2.1.1 Preliminary evidence; 2.2 Fear; 2.3 Sadness; 2.4 Disgust; 3. New data; 3.1 Experiment 1; 3.1.1 Stimuli; 3.1.2 Subjects and Procedure; 3.1.3 Results; Size perception; Emotion perception; 3.1.4 Findings of Experiment 1; 3.2 Experiment 2; 3.2.1 Stimuli; 3.2.2 Subjects and procedure; 3.2.3 Results 3.2.4 Findings of Experiment 2 and further implications; 4. Parallel encoding of emotional and linguistic information; 5. Conclusions; References; Peak alignment and surprise reading; 1. Introduction; 2. Corpus Analysis (C-ORAL-ROM); 2.1 Material; 2.2 Results; 3. Production test; 3.1 Materials; 3.2 Speakers; 3.3 Procedures; 3.4 Analysis; 3.5 Results; 4. Perception and evaluation test; 4.1 Material; 4.2 Listeners; 4.3 Procedures; 4.4 Results; 5. Discussion; References; Emotional McGurk effect and gender difference - a Swedish study; 1. Background; 2. Research questions; 3. Method; 4. Method of analysis 5. Results; 6. Summary; 7. Discussion; 8. Complicating factors in perception experiments; References; Beyond the given; 1. Introduction; 2. Theory and methodology; 2.1 Prosody defined; 2.2 The Theory of enunciative operations; 2.3 What is pertinent, what is not - or less so?; 3. Pilot corpus; 3.1 Going beyond "given" as opposed to "new" information; 3.2 The Diary corpus; 3.3 The Maps corpus; 3.4 The initial term in a series; 3.5 The presentation of an item as a continuous series; 4. The given and beyond; 4.1 Unaccented items; 4.2 The personal pronoun "she" - referent external to the dialogic couple
• Sommario/riassunto: The benefit of prosodic and additional spectral over exclusively cepstral feature information is investigated for the recognition of phonemes in eight different speaking styles reaching from informal to formal. As prosodic information is best analyzed on a supra-segmental level, the whole temporal context of a phoneme is exploited by application of statistical functionals. 521 acoustic features are likewise obtained and evaluated per descriptor and functional by either de-correlating floating search feature evaluation or classification performance: The classifier of choice are Support Vector M