Unsupervised learning of auditory filter banks using non-negative matrix factorisation

Non-negative matrix factorisation (NMF) is an unsupervised learning technique that decomposes a non-negative data matrix into a product of two lower rank non-negative matrices. The non-negativity constraint results in a parts-based and often sparse representation of the data. We use NMF to factorise a matrix with spectral slices of continuous speech to automatically find a feature set for speech recognition. The resulting decomposition yields a filter bank design with remarkable similarities to perceptually motivated designs, supporting the hypothesis that human hearing and speech production are well matched to each other. We point out that the divergence cost criterion used by NMF is linearly dependent on energy, which may influence the design. We will however argue that this does not significantly affect the interpretation of our results. Furthermore, we compare our filter bank with several hearing models found in literature. Evaluating the filter bank for speech recognition shows that the same recognition performance is achieved as with classical MEL- based features.