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Tone is the use of pitch in language to distinguish lexical or grammatical meaning—that is, to distinguish or to inflect words. [1] All oral languages use pitch to express emotional and other para-linguistic information and to convey emphasis, contrast and other such features in what is called intonation, but not all languages use tones to distinguish words or their inflections, analogously ...
Only two tones, associated with pitch accents, are recognised, these being H (high) and L (low); all other tonal contours are made up of combinations of H, L and some other modifying elements. In addition to the two tones mentioned above, the phonological system includes "break indices" used to mark the boundaries between prosodic elements.
In phonetics, contour describes speech sounds that behave as single segments but make an internal transition from one quality, place, or manner to another. Such sounds may be tones, vowels, or consonants. Many tone languages have contour tones, which move from one level to another. For example, Mandarin Chinese has four lexical tones. The high ...
This source mentions both tone and pitch accent: page 60 says that [i]n words and phrases of more than one syllable the stretches of tones formed accentual patterns, much like those of Japanese pitch accent, and that makes Korean different from a tone language such as Vietnamese or classical Chinese.
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Actio – canon #5 in Cicero's list of rhetorical canons; traditionally linked to oral rhetoric, referring to how a speech is given (including tone of voice and nonverbal gestures, among others). Ad hominem – rebutting an argument by attacking the character, motive, or other attribute of the person making it rather than the substance of the ...
Ricky Hatton laughed his way through his Hall of Fame induction speech, marveling at the places boxing took him and the thousands of his fans that would always follow.
[28] Native speakers listening to actors reading emotionally neutral text while projecting emotions correctly recognized happiness 62% of the time, anger 95%, surprise 91%, sadness 81%, and neutral tone 76%. When a database of this speech was processed by computer, segmental features allowed better than 90% recognition of happiness and anger ...