Audio Classification for Blackfoot Language Analysis
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.
KeywordsCumulative Distribution Function Positive Instance Audio Feature Concept Detection Decision Fusion
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