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This paper describes an original dataset of children's speech, collected through the use of JIBO, a social robot. The dataset encompasses recordings from 110 children, aged 4–7 years old, who participated in a letter and digit identification task and extended oral discourse tasks requiring explanation skills, totaling 21 h of session data. Spanning a 2-year collection period, this dataset contains a longitudinal component with a subset of participants returning for repeat recordings. The dataset, with session recordings and transcriptions, is publicly available, providing researchers with a valuable resource to advance investigations into child language development. 

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. 

The manner in which acoustic features contribute to perceiving speaker identity remains unclear. In an attempt to better understand speaker perception, we investigated human and machine speaker discrimination with utterances shorter than 2 seconds. Sixty-five listeners performed a same vs. different task. Machine performance was estimated with i-vector/PLDA-based automatic speaker verification systems, one using mel-frequency cepstral coefficients (MFCCs) and the other using voice quality features (VQual2) inspired by a psychoacoustic model of voice quality. Machine performance was measured in terms of the detection and log-likelihood-ratio cost functions. Humans showed higher confidence for correct target decisions compared to correct non-target decisions, suggesting that they rely on different features and/or decision making strategies when identifying a single speaker compared to when distinguishing between speakers. For non-target trials, responses were highly correlated between humans and the VQual2-based system, especially when speakers were perceptually marked. Fusing human responses with an MFCC-based system improved performance over human-only or MFCC-only results, while fusing with the VQual2-based system did not. The study is a step towards understanding human speaker discrimination strategies and suggests that automatic systems might be able to supplement human decisions especially when speakers are marked.