3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in vowel-classification-10
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Use these libraries to find vowel-classification-10 models and implementations
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This work identifies a mapping between the dynamics of wave physics and the computation in recurrent neural networks, which indicates that physical wave systems can be trained to learn complex features in temporal data, using standard training techniques for neural networks.
A knowledge-driven machine learning method that integrates spectrotemporal information of speech at the vowel-level to identify the depression outperforms baselines that model the spectrotamporal information in speech without integrating the vowel-based information, as well as ML models trained with conventional prosodic and spect rotational features.
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