Abstract
A common technique in visual object recognition is to use a sparse encoding of low-level input with a feature dictionary followed by a spatial pooling over local neighbourhoods. While some methods stack these in alternating layers within hierarchies, using these two stages alone can also produce state-of-the-art results. Following from vision, this framework is moving in to speech and audio processing tasks. We investigate the effect of architectural choices when applied to a spoken digit recognition task. We find that the unsupervised learning of features has a negligible effect on the classification, with the number of and size of the features being a greater determinant for recognition. Finally, we show that, given an optimised architecture, sparse coding performs comparably with Hidden Markov Models (HMMs) and outperforms K-means clustering.
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O’Donnell, F., Triefenbach, F., Martens, JP., Schrauwen, B. (2012). Effects of Architecture Choices on Sparse Coding in Speech Recognition. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_79
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DOI: https://doi.org/10.1007/978-3-642-33269-2_79
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