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Please use this identifier to cite or link to this item: http://hdl.handle.net/10061/7829

Title: Building an Effective Speech Corpus by Utilizing Statistical Multidimensional Scaling Method
Authors: Goshu Nagino
Makoto Shozakai
Tomoki Toda
Hiroshi Saruwatari
Kiyohiro Shikano
Keywords: speech corpus
cost effective
speaker selection
acoustic model
statistical MDS method
Issue Date: Mar-2008
Publisher: 電子情報通信学会
Journal Title: IEICE Transactions on Information and Systems
Volume: E91-D
Issue: 3
Start page: 607
End page: 614
Abstract: This paper proposes a technique for building an effective speech corpus with lower cost by utilizing a statistical multidimensional scaling method. The statistical multidimensional scaling method visualizes multiple HMM acoustic models into two-dimensional space. At first, a small number of voice samples per speaker is collected; speaker adapted acoustic models trained with collected utterances, are mapped into two-dimensional space by utilizing the statistical multidimensional scaling method. Next, speakers located in the periphery of the distribution, in a plotted map are selected; a speech corpus is built by collecting enough voice samples for the selected speakers. In an experiment for building an isolated-word speech corpus, the performance of an acoustic model trained with 200 selected speakers was equivalent to that of an acoustic model trained with 533 non-selected speakers. It means that a cost reduction of more than 62% was achieved. In an experiment for building a continuous word speech corpus, the performance of an acoustic model trained with 500 selected speakers was equivalent to that of an acoustic model trained with 1179 non-selected speakers. It means that a cost reduction of more than 57% was achieved.
URI: http://hdl.handle.net/10061/7829
URL: https://search.ieice.org/
ISSN: 0916-8532
Rights: Copyright (C) 2008 電子情報通信学会.
Text Version: publisher
Publisher DOI: 10.1093/ietisy/e91-d.3.607
Appears in Collections:情報科学研究科 / Graduate School of Information Science

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