Error analysis using decision trees in spontaneous presentation speech recognition

This paper proposes the use of decision trees for analyzing errors in spontaneous presentation speech recognition. The trees are designed to predict whether a word or a phoneme can be correctly recognized or not, using word or phoneme attributes as inputs. The trees, are constructed using training "cases" by choosing questions about attributes step by step according to the gain ratio criterion. The errors in recognizing spontaneous presentations given by 10 male speakers were analyzed, and the explanation capability of attributes for the recognition errors was quantitatively evaluated. A restricted set of attributes closely related to the recognition errors was identified for both words and phonemes.