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標題: 語音環境控制輔具系統之單字切割研究
Word Boundary Detection Analysis of Speech Environment Control Auxiliary System
作者: 劉民洋
Ming-Yang, Liu
關鍵字: speech recognition
word boundary
hidden markov model
出版社: 電機工程學系
摘要: 語音辨識在最近數十年已被廣泛的研究,本計畫為本校與國科會以及中山醫大的輔具合作計畫,完成一個中文聲控選單驅動之環境控制系統。此一系統可使行動不便的病患能夠藉由人機介面的設計,以語音的方式就可以控制家裡的家電,使得操作較為便利,並達到擴大病人居家生活的獨立性。 本論文的研究內容為中文連續語音切割,比較Elman類神經網路與大多數人使用的能量及越零率參數法,看是否在雜訊的環境中,單字切割的效果有改善。接下來就是把切割好的單字以MFCC汲取出語音的特徵參數,以連續型隱藏式馬可夫模型,分別建立各個中文單字的模型參數,最後在實際環境中做測試,實驗結果可知,在無背景雜訊下辨識率可達95%左右,而噪音環境下的測試則是因環境不同而辨識率也不同,大約有80%。
Speech recognition has been an intensively researched topic at the recent decades. This is a collaboration project between National Sciences Council, Chung Shan Medical University and our university, to deliver a Menu Based Chinese Pronunciations Control System. This system will facilitate those patients with movement difficulties via the design of the human machine interface. They can control the household electrical appliances by using the audio speech, this make the operations of the electrical appliance easier. And also promote the living life-style of the patients to be more independent. The focuses of the research are on the segmental of continuous Chinese pronunciation, the comparison the Elman Neural Network method with the popular usage of energy and zero crossing parameter method, and determine the improvement in the segmentation of a single word, under a noisy environment. The MFCC method is used to derive the characteristic parameter of pronunciations. The Hidden Markov model is used to set pattern parameter for each Chinese word respectively. At last, a testing at a real-world environment was conducted. The outcomes of the testing, showing that, the percentage of recognition under a noise-free environment will be about 95%, while at a noisy environment the percentage of recognition will be about 80%.
Appears in Collections:電機工程學系所



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