Uniform scaling as a method of vowel normalization

AbstractThe evaluation of normalization methods sometimes focuses on the maximization of vowel-space similarity. This focus can lead to the adoption of methods that erase legitimate phonetic variation from our data, that is, overnormalization. First, a production corpus is presented that highlights three types of variation in formant patterns: uniform scaling, nonuniform scaling, and centralization. Then the results of two perceptual experiments are presented, both suggesting that listeners tend to ignore variation according to uniform scaling, while associating nonuniform scaling and centralization with phonetic differences. Overall, results suggest that normalization methods that remove variation not according to uniform scaling can remove legitimate phonetic variation from vowel formant data. As a result, although these methods can provide more similar vowel spaces, they do so by erasing phonetic variation from vowel data that may be socially and linguistically meaningful, including a potential male-female difference in the low vowels in our corpus.

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“Intelligent” (statistically‐guided) algorithms for vowel normalization

The Journal of the Acoustical Society of America ◽

10.1121/1.2004674 ◽

1980 ◽

Vol 68 (S1) ◽

pp. S32-S32 ◽

Cited By ~ 1

Author(s):

Richard A. Harshman ◽

Margaret E. Lundy ◽

Sandra Ferrari Disner

Keyword(s):

Vowel Normalization

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An auditory model of vowel normalization

The Journal of the Acoustical Society of America ◽

10.1121/1.2016740 ◽

1978 ◽

Vol 63 (S1) ◽

pp. S5-S5

Author(s):

Steven Greenberg

Keyword(s):

Auditory Model ◽

Vowel Normalization

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Approach to vowel normalization

The Journal of the Acoustical Society of America ◽

10.1121/1.1995217 ◽

1975 ◽

Vol 57 (S1) ◽

pp. S3-S3 ◽

Cited By ~ 3

Author(s):

Hisashi Wakita

Keyword(s):

Vowel Normalization

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An automated approach for the optimised estimation of breast density with Dixon methods

British Journal of Radiology ◽

10.1259/bjr.20190639 ◽

2020 ◽

Vol 93 (1106) ◽

pp. 20190639 ◽

Cited By ~ 1

Author(s):

Rosie Goodburn ◽

Evanthia Kousi ◽

Alison Macdonald ◽

Veronica Morgan ◽

Erica Scurr ◽

...

Keyword(s):

Breast Density ◽

Spatial Gradient ◽

Breast Volume ◽

Absolute Increase ◽

Water Fraction ◽

Total Percentage ◽

Automated Method ◽

Image Volume ◽

Text Images ◽

Uniform Scaling

Objective: To present and evaluate an automated method to correct scaling between Dixon water/fat images used in breast density (BD) assessments. Methods: Dixon images were acquired in 14 subjects with different T1 weightings (flip angles, FA, 4°/16°). Our method corrects intensity differences between water ([Formula: see text]) and fat ([Formula: see text]) images via the application of a uniform scaling factor (SF), determined subject-by-subject. Based on the postulation that optimal SFs yield relatively featureless summed fat/scaled-water ([Formula: see text]) images, each SF was chosen as that which generated the lowest 95th-percentile in the absolute spatial-gradient image-volume of [Formula: see text] . Water-fraction maps were calculated for data acquired with low/high FAs, and BD (%) was the total percentage water within each breast volume. Results: Corrected/uncorrected BD ranged from, respectively, 10.9–71.8%/8.9–66.7% for low-FA data to 8.1–74.3%/5.6–54.3% for high-FA data. Corrected metrics had an average absolute increase in BD of 6.4% for low-FA data and 18.4% for high-FA data. BD values estimated from low- and high-FA data were closer following SF-correction. Conclusion: Our results demonstrate need for scaling in such BD assessments, where our method brought high-FA and low-FA data into closer agreement. Advances in knowledge: We demonstrated a feasible method to address a main source of inaccuracy in Dixon-based BD measurements.

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