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Cohorts and neighbors Features Analysis by synthesis Multi-resolution 3 passes 58
Detail Multi-resolution Both coarse and fine info Exemplar: k-nearest neighbors coarseness in retrieval Substatistics fineness calculated in coarse addresses 59
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Making Phonological Decisions Willi
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3 topics Finding vowel categories/f
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Long-term Memory Audition Articulat
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Using Statistics Magic! Data summar
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Finding phonemes 9
Page 11 and 12:
Recursive binary divisions. Dresher
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Classical Manchu Vowels Contrastive
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Page 19 and 20: Classical Manchu Vowels Contrastive
Page 33 and 34: 100 75 50 25 0 100 -150 -137.5 -125
Page 35 and 36: 100 100 75 75 50 50 25 25 b p 0 0 -
Page 37 and 38: 100 100 75 75 50 50 poor discrimina
Page 39 and 40: Greek Vowels F1 x F2 space for Gree
Page 41 and 42: Learning from the Primordial soup E
Page 43 and 44: English: Peterson and Barney 3000 F
Page 45 and 46: Normalization Don’t use F1 and F2
Page 47 and 48: Ratio with F3 ANOVA F1/F3 0.40 0.30
Page 49 and 50: Best split for front 0.9 How compac
Page 51 and 52: Look for phonological activity. Dre
Page 53 and 54: Frequent frames Mintz 41
Page 55 and 56: Diagnosing vowel harmony CHILDES tr
Page 57 and 58: Not done Cue for vowel harmony not
Page 59 and 60: Finding words 47
Page 61 and 62: “UG, statistics or both” Look b
Page 63 and 64: Bayes! Johnson 51
Page 65 and 66: Bayesian phonology p(UR|SR) = p(SR|
Page 67 and 68: a ï o u e i ö ü a ï o u e i ö
Page 69: Discussion 57
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