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  本文關鍵詞：多類分類支持向量機在嵌入式語音識別系統(tǒng)中的研究　出處：《太原理工大學》2012年碩士論文　論文類型：學位論文

  更多相關文章： 語音識別 支持向量機 多類分類 DM6446

【摘要】：語音識別作為一門交叉學科,在人類智能化和信息化的道路上有著不可忽視的作用。近些年,嵌入式已經成為了信息領域的研究熱點。在嵌入式系統(tǒng)中應用語音識別技術成為了語音識別發(fā)展的新方向。 語音識別技術的關鍵是解決多類分類問題,基于統(tǒng)計學習理論的支持向量機方法因為其在解決分類問題上面的獨特優(yōu)勢,已經成為了語音識別領域的研究熱點。支持向量機方法來源于統(tǒng)計學習理論,克服了傳統(tǒng)語音識別方法(人工神經網絡、隱馬爾科夫模型)的不足,在有限樣本多類分類問題中得到了廣泛的應用。支持向量機方法本來是解決二分類問題的,研究者在其基礎上推廣出了多種多類分類的方法,一對余組合分類法、對一組合分類法、決策有向無環(huán)圖組合分類法、糾錯輸出編碼多類分類、超球多類分類等方法。 本文采用DM6446開發(fā)板作為嵌入式語音開發(fā)平臺,其軟硬件性能完全能滿足語音識別技術的嵌入式開發(fā)。針對決策有向無環(huán)圖支持向量機算法和糾錯輸出編碼支持向量機算法,搭建嵌入式交叉編譯平臺。在搭建好的平臺上,將兩種支持向量機算法移植到DM6446開發(fā)板。兩種多類分類支持向量機算法分別經過兩種不同的語音庫進行語音識別實驗,均得到了較高的識別率。但由于嵌入式系統(tǒng)自己的局限性,算法所需的訓練時間較長,通過樣本預選取算法處理訓練樣本后再進行語音識別實驗,在保證良好識別率的情況下,極大地縮短了訓練所需時間,達到了預期的效果。 基于Java語言的超球支持向量機算法是多類分類方法的一個新思路。在Java程序設計中,Java虛擬機處于核心地位,正是由于虛擬機的存在,保證了Java語言在各種平臺都可以不加修改的運行。本文針對Java VM虛擬機,搭建嵌入式交叉編譯環(huán)境,成功的將虛擬機移植到嵌入式開發(fā)板,并驗證了Java類包的正確性。運用超球支持向量機算法進行語音識別實驗,得到了良好的識別結果。
[Abstract]:Speech recognition is a cross subject, plays an important role in human intelligence and information on the road. In recent years, embedded system has become a hot research field of information. The application of speech recognition technology in embedded systems has become the new direction of development of speech recognition.
The key technology of speech recognition is to solve the multi class classification problem, based on statistical learning theory and support vector machine method because of its unique advantages in solving classification problems above, it has become a research hotspot in the field of speech recognition. Support vector machine method in statistical learning source theory, to overcome the traditional speech recognition method (artificial neural network. The hidden Markov model) problems, has been widely used in the multi class classification problem of limited samples. SVM is to solve two classification problems, the researchers based on the promotion of a variety of multi class classification method, a classification method of combination of Yu, a combination classification method, decision making acyclic graph combination classification method, error correcting output encoding multi class classification, hyper sphere multi class classification method.
This paper uses the DM6446 development board as the embedded speech development platform, the software and hardware performance can meet the development of embedded speech recognition technology. Aiming at the decision directed acyclic graph support vector machine algorithm and error correcting output encoding algorithm of support vector machine, build the embedded cross compiler platform. In the platform, the two kinds of support vector machine algorithm ported to DM6446 development board. Two kinds of multi class classification algorithm of support vector machine respectively through two different speech database for speech recognition experiments were obtained with high recognition rate. But due to the limitation of the embedded system, the long training time required by the algorithm, through the sample pre selection algorithm of training samples after speech recognition experiments, in order to ensure the good recognition rate, greatly shorten the training time needed to achieve the desired results.
Based on the Java language of the hyper sphere support vector machine algorithm is a new idea of multi class classification method. In the design of Java program, the Java virtual machine is the core, it is because of the existence of the virtual machine, to ensure that the Java language can be run without modification in various platforms. According to the Java VM virtual machine. Build the embedded cross compiler environment, the success of the virtual machine is transplanted to the embedded development board, and verifies the correctness of the Java package. The use of hyper sphere support vector machine algorithm for speech recognition experiment, get good recognition results.

【學位授予單位】：太原理工大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TP368.1;TN912.34

【引證文獻】

相關博士學位論文 前1條

1 張文春;基于支持向量機—可拓學的三峽庫區(qū)豐都縣水庫塌岸預測研究[D];吉林大學;2012年

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本文編號：1377589