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  1. The problem of characterizing voice quality has long caused debate and frustration. The richness of the available descriptive vocabulary is overwhelming, but the density and complexity of the information voices convey lead some to conclude that language can never adequately specify what we hear. Others argue that terminology lacks an empirical basis, so that language-based scales are inadequate a priori. Efforts to provide meaningful instrumental characterizations have also had limited success. Such measures may capture sound patterns but cannot at present explain what characteristics, intentions, or identity listeners attribute to the speaker based on those patterns. However, some terms continually reappear across studies. These terms align with acoustic dimensions accounting for variance across speakers and languages and correlate with size and arousal across species. This suggests that labels for quality rest on a bedrock of biology: We have evolved to perceive voices in terms of size/arousal, and these factors structure both voice acoustics and descriptive language. Such linkages could help integrate studies of signals and their meaning, producing a truly interdisciplinary approach to the study of voice.

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    Free, publicly-accessible full text available February 1, 2025
  2. This study replicates and extends the recent findings of Lee, Keating, and Kreiman [J. Acoust. Soc. Am. 146(3), 1568–1579 (2019)] on acoustic voice variation in read speech, which showed remarkably similar acoustic voice spaces for groups of female and male talkers and the individual talkers within these groups. Principal component analysis was applied to acoustic indices of voice quality measured from phone conversations for 99/100 of the same talkers studied previously. The acoustic voice spaces derived from spontaneous speech are highly similar to those based on read speech, except that unlike read speech, variability in fundamental frequency accounted for significant acoustic variability. Implications of these findings for prototype models of speaker recognition and discrimination are considered.

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  3. This study compares human speaker discrimination performance for read speech versus casual conversations and explores differences between unfamiliar voices that are “easy” versus “hard” to “tell together” versus “tell apart.” Thirty listeners were asked whether pairs of short style-matched or -mismatched, text-independent utterances represented the same or different speakers. Listeners performed better when stimuli were style-matched, particularly in read speech−read speech trials (equal error rate, EER, of 6.96% versus 15.12% in conversation–conversation trials). In contrast, the EER was 20.68% for the style-mismatched condition. When styles were matched, listeners' confidence was higher when speakers were the same versus different; however, style variation caused decreases in listeners' confidence for the “same speaker” trials, suggesting a higher dependency of this task on within-speaker variability. The speakers who were “easy” or “hard” to “tell together” were not the same as those who were “easy” or “hard” to “tell apart.” Analysis of speaker acoustic spaces suggested that the difference observed in human approaches to “same speaker” and “different speaker” tasks depends primarily on listeners' different perceptual strategies when dealing with within- versus between-speaker acoustic variability.

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