https://scholars.lib.ntu.edu.tw/handle/123456789/122006
標題: | 中文口語處理技術之前瞻性研究課題( 1/3 ) | 作者: | 李琳山 | 公開日期: | 31-七月-2003 | 出版社: | 臺北市:國立臺灣大學電信工程學研究所 | 摘要: | With the rapid developments of wireless communications, it is highly desired for users to access the network information with spoken dialogue interface via hand-held devices at any time, from anywhere. One possible approach towards this goal is to perform speech feature extraction at the hand-held devices (the clients) and have all other recognition tasks and dialogue functions absorbed by the server. This report investigated distributed Chinese keyword spotting and verification under this scenario. A “phonetically distributed” Mandarin speech database including all possible Mandarin syllables and context relationships with frequencies roughly proportional to those occurring in daily Mandarin conversation is used to train a best set of vector quantization codebooks, such that the syllable recognition accuracy degradation due to quantization errors is minimized. Enhanced Chinese keyword spotting techniques were then developed using utterance verification approaches with weighting parameters optimized by MCE training. Experimental results indicated that the keyword verification approach achieved significant improvements in keyword spotting performance, and the overall results integrating vector quantization, keyword spotting and verification is quite satisfactory. 隨著無線通訊的快速發展,我們將可以透過手機等攜帶方便的工具提供的語音介面,隨時隨地取得想要的 資訊或知識。想要達到這個目標,一個可能的做法是,在使用者手上的機器這邊,做好抽取語音特徵參數 的工作,剩下的工作,諸如語音辨識或是對話介面,都交給遠端的伺服器執行。本篇論文主要探討的就是, 在這種環境之下的中文關鍵詞檢測與認證。訓練語料是一套包含所有中文音節以及其前後關係和頻率資訊 的資料庫,使用這樣的語料庫訓練一套最佳的向量量化碼本,以極小化量化錯誤對語音辨識正確率的影響。 接著提出強化的中文關鍵詞檢測的方法,概念是語音認證的技術,再加上最小分類錯誤訓練法訓練出來的 參數。 由實驗結果可以看出關鍵詞認證對關鍵詞檢測的確有顯著的幫助,而且整體向量量化、關鍵詞檢測與認證 的結果皆令人滿意。 |
URI: | http://ntur.lib.ntu.edu.tw//handle/246246/20277 | 其他識別: | 912219E002040 | Rights: | 國立臺灣大學電信工程學研究所 |
顯示於: | 電信工程學研究所 |
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912219E002040.pdf | 167.73 kB | Adobe PDF | 檢視/開啟 |
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