We evaluated three classes of speech models against a set of neural signatures. If you’re curious how they performed, stop by our poster to learn more!

Prior work has shown that speech models can predict brain responses to natural speech. However, it remains unclear whether these models also reproduce well-documented signatures of the auditory cortex.

3. We manipulated the model’s access to past and future speech cues, revealing the importance of the acoustic context and its directionality in human speech recognition.

Come to our poster to learn more!

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2. These tasks allowed us to compute the first full phoneme confusion matrix in humans at scale. This enabled the first systematic comparison of human–model phoneme confusions, revealing that humans and models share not only similar response patterns but also similar patterns of confusions.

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This work has 3 main contributions:

1. We developed new models of continuous speech recognition alongside novel behavioral tasks to compare both models and humans on speech perception without conflating speech and language.

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