Siheng Yi

This work introduces TopCap and TopNN, which use time-delay embedding and persistent homology to extract topological features (such as maximal persistence and its birth time) from speech time series; on voiced versus voiceless consonant classification, TopCap reaches accuracy comparable to some state-of-the-art neural networks on small datasets while offering greater efficiency and interpretability, and TopNN, which concatenates topological features with gated recurrent unit features, achieves higher accuracy, steadier performance, and stronger noise robustness than standard neural networks across multiple datasets and signal-to-noise ratios.

This work introduces TopCap and TopNN, which use time-delay embedding and persistent homology to extract topological features (such as maximal persistence and its birth time) from speech time series; on voiced versus voiceless consonant classification, TopCap reaches accuracy comparable to some state-of-the-art neural networks on small datasets while offering greater efficiency and interpretability, and TopNN, which concatenates topological features with gated recurrent unit features, achieves higher accuracy, steadier performance, and stronger noise robustness than standard neural networks across multiple datasets and signal-to-noise ratios.