Voiced/Unvoiced Pronunciation Judgement Based on Sparse Representation and Learning Dictionary

摘要

According to the difference between voiced and unvoiced sounds, a new method has been proposed to judge them in this paper. The study selects enough voiced and unvoiced sounds as the object of dictionary learning and employs K-SVD algorithm to construct voiced dictionary and unvoiced dictionary respectively. Then the signals to be judged are sparse represented in voiced dictionary and unvoiced dictionary. The way to distinguish the voiced and unvoiced sounds is comparing the sparsity of coefficients in two dictionaries and the results have proved the effectiveness of this method.