Comparison of Multivariate Analysis Methods as Applied to English Speech

Abstract

A newly developed factor analysis, origin-shifted factor analysis, was compared with a normal factor analysis to analyze the spectral changes of English speech. Our first aim was to investigate whether these analyses would cause differences in the factor loadings and the extracted spectral-factor scores. The methods mainly differed in whether to use cepstral liftering and an origin shift. The results showed that three spectral factors were obtained in four main frequency bands, but neither the cepstral liftering nor the origin shift distorted the essential characteristics of the factors. This confirms that the origin-shifted factor analysis is more recommendable for future speech analyses, since it would reduce the generation of noise in resynthesized speech. Our second aim was to further identify acoustic correlates of English phonemes. Our data show for the first time that the distribution of obstruents in English speech constitutes an L-shape related to two spectral factors on the three-dimensional configuration. One factor had center loadings around 4100 Hz, while the other was bimodal with peaks around 300 Hz and 2300 Hz. This new finding validates the use of multivariate analyses to connect English phonology and speech acoustics.

Erratum: Intelligibility of chimeric locally time-reversed speech: Relative contribution of four frequency bands [ JASA Express Lett. 1(6), 065201 (2021)]

“…This band corresponds to a factor with a single peak , the mid-low factor (Nakajima et al, 2017). It has been confirmed that vowels and sonorants dominate the factor (Nakajima et al, 2017;Zhang et al, 2020); thus, it is natural that band 2 has the closest connection with sonority and, hence, syllable formation. Bands 1 and 3 contributed also to intelligibility to some extent, although the contribution was less prominent compared to that of band 2.…”

“…Nakajima and colleagues (Nakajima et al, 2017;Zhang et al, 2020) took an approach to the issue entirely different from the previous approach in linguistics. Based on factor scores extracted from correlation coefficients between power fluctuations in critical-band filtered speech , they examined relationships between the factor scores and phonemic labels assigned to speech sentences.…”

Auditory grouping is necessary to understand interrupted mosaic speech stimuli

“…Crossover points on factor loading curves for all languages/dialects examined that were plotted against the filter channels yielded four frequency bands, common to eight languages/dialects. The factor scores of the three factors provided a reasonable curve on which the English phonemic categories were ordered systematically ( Nakajima et al , 2017 ; Zhang et al , 2020 ). One of the factors highly correlated with sonority scales ( Nakajima et al , 2017 ) and the frequency band which corresponded to the factor was critical in maintaining high intelligibility ( Matsuo et al , 2020 ; Ueda and Matsuo, 2021 ).…”