Recent work on perceptual learning for speech has suggested that while high-variability training typically results in generalization, low-variability exposure can sometimes be sufficient for cross-talker generalization. We tested predictions of a similarity-based account, according to which, generalization depends on training-test talker similarity rather than on exposure to variability. We compared perceptual adaptation to second-language (L2) speech following single- or multiple-talker training with a round-robin design in which four L2 English talkers from four different first-language (L1) backgrounds served as both training and test talkers. After exposure to 60 L2 English sentences in one training session, cross-talker/cross-accent generalization was possible (but not guaranteed) following either multiple- or single-talker training with variation across training-test talker pairings. Contrary to predictions of the similarity-based account, adaptation was not consistently better for identical than for mismatched training-test talker pairings, and generalization patterns were asymmetrical across training-test talker pairs. Acoustic analyses also revealed a dissociation between phonetic similarity and cross-talker/cross-accent generalization. Notably, variation in adaptation and generalization related to variation in training phase intelligibility. Together with prior evidence, these data suggest that perceptual learning for speech may benefit from some combination of exposure to talker variability, training-test similarity, and high training phase intelligibility.
This content will become publicly available on June 1, 2025
Speech recognition by both humans and machines frequently fails in non-optimal yet common situations. For example, word recognition error rates for second-language (L2) speech can be high, especially under conditions involving background noise. At the same time, both human and machine speech recognition sometimes shows remarkable robustness against signal- and noise-related degradation. Which acoustic features of speech explain this substantial variation in intelligibility? Current approaches align speech to text to extract a small set of pre-defined spectro-temporal properties from specific sounds in particular words. However, variation in these properties leaves much cross-talker variation in intelligibility unexplained. We examine an alternative approach utilizing a perceptual similarity space acquired using self-supervised learning. This approach encodes distinctions between speech samples without requiring pre-defined acoustic features or speech-to-text alignment. We show that L2 English speech samples are less tightly clustered in the space than L1 samples reflecting variability in English proficiency among L2 talkers. Critically, distances in this similarity space are perceptually meaningful: L1 English listeners have lower recognition accuracy for L2 speakers whose speech is more distant in the space from L1 speech. These results indicate that perceptual similarity may form the basis for an entirely new speech and language analysis approach.
more » « less- Award ID(s):
- 2219843
- NSF-PAR ID:
- 10516293
- Publisher / Repository:
- The Journal of the Acoustical Society of America
- Date Published:
- Journal Name:
- The Journal of the Acoustical Society of America
- Volume:
- 155
- Issue:
- 6
- ISSN:
- 0001-4966
- Page Range / eLocation ID:
- 3915 to 3929
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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