Discovering Phone Patterns in Spoken Utterances by Non-Negative Matrix Factorization

Model order estimation using Bayesian NMF for discovering phone patterns in spoken utterances

In earlier work, we have shown that vocabulary discovery from spoken utterances and subsequent recognition of the acquired vocabulary can be achieved through Non-negative Matrix Factorization (NMF). An open issue for this task is to determine automatically how many different word representations should be included in the model. In this paper, Bayesian NMF is applied to estimate the model order....

Discovering hierarchical speech features using convolutional non-negative matrix factorization

Discovering a representation that reflects the structure of a dataset is a first step for many inference and learning methods. This paper aims at finding a hierarchy of localized speech features that can be interpreted as parts. Non-negative matrix factorization (NMF) has been proposed recently for the discovery of parts-based localized additive representations. Here, I propose a variant of thi...

Non-negative matrix factorization

Bayesian Non-negative Matrix Factorization

We present a Bayesian treatment of non-negative matrix factorization (NMF), based on a normal likelihood and exponential priors, and derive an efficient Gibbs sampler to approximate the posterior density of the NMF factors. On a chemical brain imaging data set, we show that this improves interpretability by providing uncertainty estimates. We discuss how the Gibbs sampler can be used for model ...

Robust non-negative matrix factorization

Non-negative matrix factorization (NMF) is a recently popularized technique for learning partsbased, linear representations of non-negative data. The traditional NMF is optimized under the Gaussian noise or Poisson noise assumption, and hence not suitable if the data are grossly corrupted. To improve the robustness of NMF, a novel algorithm named robust nonnegative matrix factorization (RNMF) i...