Talker variability facilitates the statistical learning of speech sounds

Natural speech contains many sources of acoustic variability both within and between talkers, which challenges speech recognition in some contexts but may facilitate language understanding in novel listening situations. Despite this ubiquitous variability, most previous studies that have examined the ability to extract sound patterns in speech—known as statistical learning—have used highly controlled, artificial, monotonic streams of syllables. Thus, it is unknown whether variability in speech may help or hinder statistical learning – an important question to resolve if statistical learning does indeed play a role in the segmentation of naturally spoken language, as widely theorized. Here, we assessed whether the use of naturally produced, variable speech sounds produced by multiple talkers benefits or impairs statistical learning, including the ability to generalize patterns to a novel talker. During training, participants listened to approximately 12 minutes of continuous speech made up of repeating trisyllabic words, spoken either by a single talker (single-talker condition) or four talkers speaking for three minutes each (multiple-talker condition). Post-training, all participants completed three assessments of learning: (1) an explicit familiarity rating task, (2) an explicit forced-choice recognition task, and (3) an implicit syllable target detection task. Results indicated that participants in both training conditions showed evidence of statistical learning across all assessments, providing an important demonstration that statistical learning is robust to additional variability in the speech signal. Further, in both the forced-choice recognition task and target detection task, participants in the multiple-talker condition showed evidence of facilitated statistical learning, particularly when listening to a novel talker. In the familiarity rating task, performance was comparable between conditions; however, participants trained with multiple talkers were less likely to conflate word familiarity with talker voice familiarity. Overall, these results suggest that training with multiple talkers can improve aspects of statistical learning across multiple measures of learning.

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