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Hierarchical Decoding of Perceived Speech From Non-Invasive Brain Recordings.

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    This study introduces a hierarchical decoding framework for speech perception using M/EEG signals. Integrating mel-spectrogram, Wav2vec 2.0, and GPT-2 representations significantly improved decoding accuracy, outperforming previous methods.

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    Area of Science:

    • Neuroscience
    • Computational Linguistics
    • Signal Processing

    Background:

    • Speech perception is a hierarchical process involving multiple brain regions.
    • Existing speech decoding methods rarely incorporate this hierarchical nature.
    • Non-invasive brain signal analysis (M/EEG) offers insights into speech perception.

    Purpose of the Study:

    • To propose a novel hierarchical decoding framework for speech perception.
    • To integrate diverse speech representations (mel-spectrogram, Wav2vec 2.0, GPT-2) for improved decoding.
    • To evaluate the framework's performance against existing methods.

    Main Methods:

    • Developed ConvConcatNet, a hierarchical decoder using iterative convolution and concatenation.
    • Employed contrastive learning to align neural features with speech representations.
    • Validated the framework on Chinese MEG (SMN4Lang) and Dutch EEG (SparrKULee) datasets.

    Main Results:

    • Wav2vec 2.0 representation showed high decoding performance.
    • Integrating all three representations (mel-spectrogram, Wav2vec 2.0, GPT-2) substantially boosted accuracy.
    • GPT-2 representation improved decoding for context-dependent words.
    • ConvConcatNet achieved state-of-the-art Top-1 accuracy: 35.6% (MEG) and 20.0% (EEG).

    Conclusions:

    • A hierarchical approach combining diverse speech features enhances non-invasive speech decoding.
    • The proposed ConvConcatNet framework effectively extracts and integrates neural patterns.
    • This work advances the understanding of speech perception through brain signal decoding.