Stimulus reconstruction-based DNNs overestimate auditory attention decoding on unbalanced EEG datasets; LOPEO cross-validation prevents the inflation.
Improv- ing grapheme-to-phoneme conversion through in-context knowl- edge retrieval with large language models,
2 Pith papers cite this work. Polarity classification is still indexing.
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SPARCLE builds speaker-aware grapheme representations by contrastively aligning characters with Wav2Vec2 acoustic embeddings conditioned on speaker identity, replacing G2P for TTS and halving WER in low-resource cases.
citing papers explorer
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Decoding Stimulus Reconstruction-Based Auditory Attention Robustly in Unbalanced EEG Datasets
Stimulus reconstruction-based DNNs overestimate auditory attention decoding on unbalanced EEG datasets; LOPEO cross-validation prevents the inflation.
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SPARCLE: SPeaker-aware Aligned Representations via Contrastive Language Embeddings
SPARCLE builds speaker-aware grapheme representations by contrastively aligning characters with Wav2Vec2 acoustic embeddings conditioned on speaker identity, replacing G2P for TTS and halving WER in low-resource cases.