Fig 1.
A) Words in the normative database (BAWL) were segmented and coded for the presence or absence of a given phoneme (here exemplified by the phoneme /t/). The phonemes were analyzed one-by-one to determine their potential effect on valence and arousal ratings. The potential affective effect caused by each single phoneme (i.e. PAV) was computed as the average of valence or arousal ratings of words containing this specific phoneme. The PAP of each word was calculated as the average of all its PAVs. B) Words were synthesized and their extracted acoustic features were used in two multiple linear regression models as predictors for the PAP of arousal (right) and valence (left). The acoustic variables (11 in total) accounted for 27.9% and 23.7% of the variance in PAParo and PAPval respectively (study 1).
Fig 2.
Acoustic features of pseudowords (N = 11) significantly predicted the ratings of their affective sound: 11.2% for valence (left) and 56.3% for arousal (right).
Fig 3.
A) Acoustic profiles were constructed (using correlation cell plot) based on the strength and direction of correlations between the estimated effect of words’ phonology on the evaluation of their affective meaning (i.e. Phonological Affective Potential: PAP), the two measures of words’ affective sound (i.e. Affective Sound-Ratings: AS-R [study 2a], Affective Sound-Predicted: AS-P [study 2b]), and ratings of words’ affective meaning (i.e. Affective Meaning-Ratings: AM-R) on the one hand, and 11 acoustic variables on the other hand (left for valence, right for arousal). Acoustic features that significantly correlated with the PAP, AS-R, AS-P, and AM-R always show associations in the same direction, suggesting that acoustic features underlying the affective sound of words contribute in similar ways to the constitution of affective meaning of these words. B) The correlation probabilities are shown in the table. Correlations not surviving Bonferroni correction for multiple comparisons are marked with “BF” (Bonferroni Failed). Abbreviations: BW = Bandwidth, SD = standard deviation, Spec = Spectral, CoG = Centre of Gravity, r = correlation coefficient.
Fig 4.
A) The time course of sound intensity for the words “Gift /g ɪ f t/ (gift)” and “Stich /ʃ t ɪ ç/ (stab)” (top, yellow lines) compared to their counterparts “See /z e:/ (lake)” and “Lohn /l oː n/ (wage)” (bottom, red lines). Short vowels, plosives, and voiceless consonants (as in “Gift” and “Stich”) possess smaller integrals of sound energy, whereas sustained high amplitude (see red lines) results in larger sound intensity. This relationship between phonetic features and sound intensity, together with the relationship between sound intensity and ‘affective sound’ of words, explains the harsh sound of words containing short vowels, plosives, and voiceless consonants. B) Spectral analysis shows that hissing sibilants in a word increase the sound’s center of gravity (i.e. the magnitude-weighted mean of the frequencies present in the signal), which makes words including this category of phonemes sound harsh and negative (blue line Zwist /ts v ɪ s t/ (strife) vs. green line Lieb /l iː p/ (kind)).