Graph-based learning for phonetic classification
Andrie Alexandrescu, Katrin Kirchhoff · 2007
We introduce graph-based learning for acoustic-phonetic classification. In graph-based learning, training, and test data points are jointly represented in a wieghted undirected graph characterized by a weight matrix indicating similarties between different samples. Classification of test samples is achieved by label propagation over the entire graph. Although this learning technique is commnly applied in semi- supervised settings, we show how it can also be as a post- processing step to a supervised classifier by imposing additional regularization constraints based on the underlying data manifold. We also present a technique to adapt graph-based learning to large datasets and evaluate our system on a vowel classification task. Our results show that graph-based learning improves significantly over state-of-the-art baselines.