The purpose of this study is to visualize the similarities between speech corpora. Speech data are indispensable for promoting speech research. A wide variety of speech corpora has recently been developed in many countries. Corpus diversification has given users many choices for corpus selection. In order for users to easily utilize these various corpora, we propose a new feature visualization method based on the corpus attribute. First, we listed eight attributes of the speech corpora. Then, we selected a few items for each attribute resulting in 58 items in all. Each item takes on a '1' or '0' depending on whether the corpus has the attribute or not. The set of corpus features is represented as a 58-dimensional vector. Then, the vectors are converted into a similarity matrix and analyzed using a multidimensional scaling method (MDS).We analyzed the speech corpora distributed by the Speech Resources Consortium (NII-SRC). The results showed that it is possible to visualize the similarities between multiple speech corpora using the proposed method. We also tested the effectiveness of the proposed method by analyzing six imaginary corpora having some specified attributes. This result will facilitate the idea of being able to search a specific corpus according to a user's needs.
Bibliographic reference. Yamakawa, Kimiko / Matsui, Tomoko / Itahashi, Shuichi (2008): "MDS-based visualization method for multiple speech corpora", In INTERSPEECH-2008, 1666-1669.