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We explored a database covering seven dialects of British and Irish English and three different styles of speech to find acoustic correlates of prominence. We built classifiers, trained the classifiers on human prominence/nonprominence judgments, and then evaluated how well they behaved. The classifiers operate on 452 ms windows centered on syllables, using(More)
Cantonese is a major Chinese dialect with a complicated tone system. This research focuses on quantitative modeling of Cantonese tones. It uses Stem-ML, a language-independent framework for quantitative intonation modeling and generation. A set of F 0 prediction models are built, and trained on acoustic data. The prediction error is about 11 Hz or 1(More)
We describe models of Mandarin prosody that allow us to make quantitative measurements of prosodic strengths. These models use Stem-ML, which is a phenomenological model of the muscle dynamics and planning process that controls the tension of the vocal folds, and therefore the pitch of speech. Because Stem-ML describes the interactions between nearby tones,(More)
Although the pitch of the human voice is continuously variable, some linguists contend that intonation in speech is restricted to a small, limited set of patterns. This claim is tested by asking subjects to mimic a block of 100 randomly generated intonation contours and then to imitate themselves in several successive sessions. The produced f0 contours(More)
Which acoustic properties of the speech signal differ between rhythmically prominent syllables and non-prominent ones? A production experiment was conducted to identify these acoustic properties. Subjects read out repetitive text to a metronome, trying to match stressed syllables to its beat. The analysis searched for the function of the speech signal that(More)
We compare 15 measures of speech rhythm based on an automatic segmentation of speech into vowel-like and consonant-like regions. This allows us to apply identical segmentation criteria to all languages and compute rhythm measures over a large corpus. It may also approximate more closely the segmentation available to pre-lexical infants, who have been(More)