Do some languages sound more beautiful than others?

Abstract

Italian is sexy, German is rough-but how about Páez or Tamil? Are there universal phonesthetic judgments based purely on the sound of a language, or are preferences attributable to language-external factors such as familiarity and cultural stereotypes? We collected 2,125 recordings of 228 languages from 43 language families, including 5 to 11 speakers of each language to control for personal vocal attractiveness, and asked 820 native speakers of English, Chinese, or Semitic languages to indicate how much they liked these languages. We found a strong preference for languages perceived as familiar, even when they were misidentified, a variety of cultural-geographical biases, and a preference for breathy female voices. The scores by English, Chinese, and Semitic speakers were weakly correlated, indicating some cross-cultural concordance in phonesthetic judgments, but overall there was little consensus between raters about which languages sounded more beautiful, and average scores per language remained within ±2% after accounting for confounds related to familiarity and voice quality of individual speakers. None of the tested phonetic features-the presence of specific phonemic classes, the overall size of phonetic repertoire, its typicality and similarity to the listener's first language-were robust predictors of pleasantness ratings, apart from a possible slight preference for nontonal languages. While population-level phonesthetic preferences may exist, their contribution to perceptual judgments of short speech recordings appears to be minor compared to purely personal preferences, the speaker's voice quality, and perceived resemblance to other languages culturally branded as beautiful or ugly.

Keywords: cross-cultural; language attitudes; phonesthetics; voice.

Fig. 1.

Included languages (N = 228) colored by language family (N = 43). See SI Appendix, Table S1 for the full list.

Fig. 2.

Pleasantness scores by speakers of English, Chinese, and Semitic languages are weakly correlated. (A and B) Conditional scores of 228 languages (A) and 43 language families (B) after excluding all languages with familiarity >20% per group and controlling for five robust acoustic predictors of the ratings: cepstral peak prominence, entropy, spectral novelty, pitch, and pitch variability (SI Appendix, Fig. S9 and Table S5). Pearson’s correlations with 95% CI and blue regression lines are calculated pointwise from posterior distributions of centered conditional language scores from two separate mixed models, not merely from the most credible point estimates (solid points = medians of posterior distributions). (C and D) Conditional scores averaged across all three groups of listeners highlight some outliers among languages (C) and families (D). The x-coordinate is only added to reduce clutter. All scores are on a scale of 0 to 100.

Fig. 3.

Phonetic features do not have a consistent effect on pleasantness ratings. All shown predictors are tested simultaneously in one multilevel multiple regression model per listener group after excluding all languages with familiarity over 20% per group and controlling for acoustic predictors. Each point shows the predicted effect of changing one phonetic feature, while holding all other predictors constant, on the pleasantness score of a single recording. Medians of posterior distribution and 95% CIs from four mixed models. To focus on the most robust effects, we grayed out the points with <95% of posterior probability to one side of zero. N (trials/languages) = 16,792/198 for English, 15,928/210 for Chinese, 12,457/164 for Semitic, and 46,928/199 for all groups combined. The phonetic features are explained in SI Appendix, Table S3.