Abstract
Blackfoot is an endangered Native American language. It is important to document this language and with it, the Blackfoot culture. In this paper, an effective subspace-based concept mining framework (SCM) is used to help Blackfoot language analysis via audio classification. The core of SCM is a subspace based modeling, classification and decision fusion mechanism which is applied to the audio features for pattern discovery. It adaptively selects non-consecutive principal dimensions to form an accurate modeling of a representative subspace based on statistical information analysis and refines the training data set via self-learning. After the classification process, a decision fusion process is applied to traverse the results from individual classifiers and to boost classification accuracy.
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Chen, M., Miyashita, M. (2012). Audio Classification for Blackfoot Language Analysis. In: Qian, Z., Cao, L., Su, W., Wang, T., Yang, H. (eds) Recent Advances in Computer Science and Information Engineering. Lecture Notes in Electrical Engineering, vol 124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25781-0_56
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DOI: https://doi.org/10.1007/978-3-642-25781-0_56
Publisher Name: Springer, Berlin, Heidelberg
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