A predictive learning model can simulate temporal dynamics and context effects found in neural representations of continuous speech
<|reference_start|>A predictive learning model can simulate temporal dynamics and context effects found in neural representations of continuous speech: Speech perception involves storing and integrating sequentially presented items. Recent work in cognitive neuroscience has identified temporal and contextual characteristics in humans' neural encoding of speech that may facilitate this temporal processing. In this study, we simulated similar analyses with representations extracted from a computational model that was trained on unlabelled speech with the learning objective of predicting upcoming acoustics. Our simulations revealed temporal dynamics similar to those in brain signals, implying that these properties can arise without linguistic knowledge. Another property shared between brains and the model is that the encoding patterns of phonemes support some degree of cross-context generalization. However, we found evidence that the effectiveness of these generalizations depends on the specific contexts, which suggests that this analysis alone is insufficient to support the presence of context-invariant encoding.<|reference_end|>
@article{liu2024a,
    title={A predictive learning model can simulate temporal dynamics and context
  effects found in neural representations of continuous speech},
    author={Oli Danyi Liu, Hao Tang, Naomi Feldman, Sharon Goldwater},
    journal={arXiv preprint arXiv:2405.08237},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08237},
    primaryClass={cs.CL cs.SD eess.AS}
}