Topology combined machine learning for consonant recognition
Pingyao Feng Siheng Yi Qingrui Qu Zhiwang Yu Yifei Zhu
In artificial-intelligence-aided signal processing, existing deep learning
models often exhibit a black-box structure, and their validity and
comprehensibility remain elusive. The integration of topological methods,
despite its relatively nascent application, serves a dual purpose of making
models more interpretable as well as extracting structural information from
time-dependent data for smarter learning. Here, we provide a transparent and
broadly applicable methodology, TopCap, to capture the most salient topological
features inherent in time series for machine learning. Rooted in
high-dimensional ambient spaces, TopCap is capable of capturing features rarely
detected in datasets with low intrinsic dimensionality. Applying time-delay
embedding and persistent homology, we obtain descriptors which encapsulate
information such as the vibration of a time series, in terms of its variability
of frequency, amplitude, and average line, demonstrated with simulated data.
This information is then vectorised and fed into multiple machine learning
algorithms such as k-nearest neighbours and support vector machine. Notably, in
classifying voiced and voiceless consonants, TopCap achieves an accuracy
exceeding 96% and is geared towards designing topological convolutional layers
for deep learning of speech and audio signals.