@techreport{oai:ipsj.ixsq.nii.ac.jp:00067058,
 author = {岡本, 悠 and 柘植, 覚 and 堀内, 靖雄 and 黒岩, 眞吾 and Haruka, Okamoto and Satoru, Tshge and Yasuo, Horiuchi and Shingo, Kuroiwa},
 issue = {27},
 month = {Dec},
 note = {本論文では，順位統計量を用いた話者照合手法を紹介する．さらに，順位統計量を用いた話者照合手法における照合コストを下げるためのコホート話者の選択方法について提案する．コホート話者は申告者の音声に対してシステムに登録された不特定多数の話者モデル （GMM） との尤度の順位を基準に作成する．評価実験として，科学警察研究所が構築した大規模話者骨導音声データベースに収録されている男性 283 名の気導音声を用いて実験を行った．従来手法では，全話者 283 名による順位統計量で算出した minDCF が 0.0092 に対して，提案手法では平均 57 名の順位統計量で 0.0098，101 名の順位統計量で 0.0094 という同等の性能を達成した．また，照合スコアとして T-norm を用いた場合の minDCF が 0.0154 だった．, In this paper, we introduce a novel speaker verification method which determines whether a claimer is accepted or rejected by the rank of the claimer in a large number of speaker models instead of score normalization, such as T-norm and Z-norm. The method has advantages over the standard T-norm in speaker verification accuracy. However, it needs much computation time as well as T-norm that needs calculating likelihoods for many cohort models. Hence, we also discuss the speed-up the method that selects cohort speakers for each target speaker in the training stage. This data driven approach can significantly reduce computation time resulting in faster speaker verification decision. We conducted text-independent speaker verification experiments using large-scale Japanese speaker recognition evaluation corpus constructed by National Research Institute of Police Science. From the corpus, we used utterances collected from 283 Japanese males. As results, the proposed method whose the number of cohort speaker is 57 achieved an minDCF of 0.0098, while using 282 speakers as cohort speaker obtained 0.0092 and T-norm obtained 0.0154.},
 title = {順位統計量を用いた話者照合のためのコホート話者選択方法},
 year = {2009}
}