Graph-based learning for phonetic classification

We introduce graph-based learning for acoustic-phonetic classification. In graph-based learning, training and test data points are jointly represented in a weighted undirected graph characterized by a weight matrix indicating similarities between different samples. Classification of test samples is achieved by label propagation over the entire graph. Although this learning technique is commonly applied in semi-supervised settings, we show how it can also be used as a postprocessing 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.