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| 講演名 | 2011-01-28 11:00 Iterative Mapping Function Estimation and Environment Structure Refinement in the Online Phase of the ESSEM Approach ○Yu Tsao・Ryosuke Isotani・Hisashi Kawai・Satoshi Nakamura（NICT）  SP2010-112 |
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| 抄録 | （和） | Recently, we proposed an ensemble speaker and speaking environment modeling (ESSEM) approach to improve automatic speech recognition (ASR) performance under noisy testing conditions. The ESSEM approach consists of offline and online phases. For the offline, ESSEM prepares an environment structure. In our previous study, we have developed environment clustering and environment partitioning algorithms to further improve the environment structure. For the online, ESSEM estimates a mapping function to obtain a set of acoustic models that matches the testing condition based on the offline prepared environment structure. In our previous study, we have studied several techniques to enhance the accuracy of mapping function estimation. In this study, we further improve the ESSEM online phase by using a two-stage optimization procedure that iteratively calculates the mapping function and refines the environment structure. We evaluated the proposed method on the Aurora-2 task. When compared with the conventional ESSEM (single-stage), clear improvements are observed by using either maximum likelihood (ML) or maximum a posteriori (MAP) criteria for the environment structure refinement procedure. |
| （英） | Recently, we proposed an ensemble speaker and speaking environment modeling (ESSEM) approach to improve automatic speech recognition (ASR) performance under noisy testing conditions. The ESSEM approach consists of offline and online phases. For the offline, ESSEM prepares an environment structure. In our previous study, we have developed environment clustering and environment partitioning algorithms to further improve the environment structure. For the online, ESSEM estimates a mapping function to obtain a set of acoustic models that matches the testing condition based on the offline prepared environment structure. In our previous study, we have studied several techniques to enhance the accuracy of mapping function estimation. In this study, we further improve the ESSEM online phase by using a two-stage optimization procedure that iteratively calculates the mapping function and refines the environment structure. We evaluated the proposed method on the Aurora-2 task. When compared with the conventional ESSEM (single-stage), clear improvements are observed by using either maximum likelihood (ML) or maximum a posteriori (MAP) criteria for the environment structure refinement procedure. | |
| キーワード | （和） | ESSEM / MAP-based ESSEM / acoustic model adaptation / environment modeling / robustness against noise / / / |
| （英） | ESSEM / MAP-based ESSEM / acoustic model adaptation / environment modeling / robustness against noise / / / | |
| 文献情報 | 信学技報, vol. 110, no. 401, SP2010-112, pp. 55-58, 2011年1月. | |
| 資料番号 | SP2010-112 | |
| 発行日 | 2011-01-20 (SP) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| 著作権に ついて |
技術研究報告に掲載された論文の著作権は電子情報通信学会に帰属します．(許諾番号：10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| PDFダウンロード | SP2010-112 |
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