Newer, larger deep learning models show no consistent gains over older architectures for speech emotion recognition across two naturalistic benchmarks, with results sensitive to model selection and hyperparameters.
However, as before, the Spearman’s � between noisy UAR and year of publication (���), MACs ( ���), and � of parameters ( ���) was extremely low
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Charting 15 years of progress in deep learning for speech emotion recognition: A replication study
Newer, larger deep learning models show no consistent gains over older architectures for speech emotion recognition across two naturalistic benchmarks, with results sensitive to model selection and hyperparameters.