博碩士論文 90541013 完整後設資料紀錄

DC 欄位 語言
DC.contributor電機工程學系zh_TW
DC.creator陳子和zh_TW
DC.creatorZi-He Chenen_US
dc.date.accessioned2007-6-20T07:39:07Z
dc.date.available2007-6-20T07:39:07Z
dc.date.issued2007
dc.identifier.urihttp://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=90541013
dc.contributor.department電機工程學系zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract在公共電話網路中,語者辨認系統通常會遇到話筒不匹配和辨認語料不足的問題。為增進語者辨認系統之強健性,我們提出一融合下層聲學與上層韻律訊息之架構,利用韻律訊息特徵分析(latent prosody analysis, LPA),量測不同語者間的韻律模型距離,並融合聲學模型(GMM)與韻律模型分數得到最後的辨識結果。LPA 主要是利用資訊檢索的概念將SID 問題轉化成全文檢索的問題,經由下列三步驟(1) 韻律訊息標示化( tokenization), (2) 韻律訊息分析(LPA)及(3)語者檢索(speaker retrieval) 實現利用韻律訊息之強健性語者辨識。 實驗使用 Handset TIMIT(HTIMIT)語料庫,以leave-one-out方式輪流使用九種不同的話筒當作未知話筒,驗證所提出之方法。實驗結果顯示,若以傳統 maximum likelihood a priori handset knowledge interpolation (ML-AKI) 的方法當作基礎(baseline),語者辨識率將可傳統pitch-GMM或 prosody bi-gram modeling 方法優異,無論對已知話筒和未知話筒皆能有效改善系統之強健性。zh_TW
dc.description.abstractHandsets that are not seen in the training phase (unseen handsets) are significant sources of performance degradation for speaker identification (SID) applications in the telecommunication environment. In this thesis, a novel latent prosody analysis (LPA) approach to automatically extract the most discriminative prosody cues for assisting in conventional spectral feature-based SID is proposed. The concept of the LPA approach is to transform the SID problem into a full-text document retrieval-like task via (1) prosodic contour tokenization, (2) latent prosody analysis, and (3) speaker retrieval. Experimental results of the phonetically balanced, read-speech, handset-TIMIT (HTIMIT) database demonstrated that the proposed method of fusing the LPA prosodic feature-based SID systems with maximum likelihood a priori handset knowledge interpolation (ML-AKI) spectral feature-based SID outperformed both the pitch and energy Gaussian mixture model (Pitch-GMM) and the bi-gram of the prosodic state (bi-gram) counterparts for both cases of counting all and only unseen handsets.en_US
DC.subject語者辨識zh_TW
DC.subject韻律訊息zh_TW
DC.subjectspeaker identificationen_US
DC.subjectprosodic informationen_US
DC.title利用韻律訊息之強健性語者辨識zh_TW
dc.language.isozh-TWzh-TW
DC.titleLatent Prosody Analysis for Robust Speaker Identificationen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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