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Title: The acoustics of place of articulation in English plosives
Authors: McCarthy, Daniel Timothy Pio Denis
Issue Date: 2019
Publisher: Newcastle University
Abstract: This thesis investigates certain aspects of the acoustics of plosives’ place of articulation that have not been addressed by most previous studies, namely: 1. To test the performance of a technique for collapsing F2onset and F2mid into a single attribute, termed F2R. Results: F2R distinguishes place with effectively the same accuracy as F2onset+F2mid, being within ±1 percentage point of F2onset+F2mid at its strongest over most of the conditions examined. 2. To compare the strength of burst-based attributes at distinguishing place of articulation with and without normalization by individual speaker. Results: Lobanov normalization on average boosted the classification of individual attributes by 1.4 percentage points, but this modest improvement shrank or disappeared when the normalized attributes were combined into a single classification. 3. To examine the effect of different spectral representations (Hz-dB, Bark-phon, and Bark-sone) on the accuracy of the burst attributes. The results are mixed but mostly suggest that the choice between these representations is not a major factor in the classification accuracy of the attributes (mean difference of 1 to 1.5 percentage points); the choice of frequency region in the burst (mid versus high) is a far more important factor (13 percentage-point difference in mean classification accuracy). 4. To compare the performance of some traditional-phonetic burst attributes with the first 12 coefficients of the discrete cosine transform (DCT). The motivation for this comparison is that phonetic science has a long tradition of developing burst attributes that are tailored to the specific task of extracting place-of-articulation information from the burst, whereas automatic speech recognition (ASR) has long used attributes that are theoretically expected to capture more of the variance in the burst. Results: the DCT coefficients yielded a higher burst classification accuracy than the traditional phonetic attributes, by 3 percentage points.
Description: PhD Thesis
Appears in Collections:School of Education, Communication and Language Sciences

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