本文選題：語音識別 + 最小二乘支持向量機��； 參考：《太原理工大學(xué)》2014年碩士論文

【摘要】：語音識別是一種最直接、最便捷的人機交互手段,屬于多維模式識別的范疇。最小二乘支持向量機是機器學(xué)習領(lǐng)域目前研究較熱的一種模式識別算法,作為標準支持向量機的一種擴展,具有小樣本學(xué)習、能夠避免“高維維數(shù)災(zāi)難”和模型訓(xùn)練算法簡單易實現(xiàn)的優(yōu)點,因此適用于復(fù)雜的語音信號的識別。但其具有解的稀疏性缺失的缺點,造成模型復(fù)雜度的提高和系統(tǒng)識別速度的降低,本文針對這個問題展開了研究,具體研究內(nèi)容如下： (1)深入研究了語音識別系統(tǒng)和最小二乘支持向量機原理,將最小二乘支持向量機引入到語音識別系統(tǒng)中,克服了傳統(tǒng)語音識別方法中隱馬爾可夫模型需要先驗分布知識、人工神經(jīng)網(wǎng)絡(luò)容易出現(xiàn)“過學(xué)習”的缺陷。 (2)仔細研究了模型參數(shù)對系統(tǒng)的學(xué)習能力和泛化能力的重要性,提出采用粒子群全局優(yōu)化算法結(jié)合K折交叉驗證的方案進行最佳參數(shù)尋優(yōu),避免了人工手動調(diào)試復(fù)雜和網(wǎng)格算法耗時長的問題。 (3)在深入研究最小二乘支持向量機稀疏性缺失的原因和語音樣本特征維數(shù)對模型性能影響的基礎(chǔ)上,提出采用基于獨立成分分析的最小二乘支持向量機稀疏化方法。該方法首先采用獨立成分分析方法進行語音特征降維；然后在模型訓(xùn)練之后采用基于獨立成分分析的快速剪枝算法對核矩陣進行約簡,約簡過程中采用峰度和偏度的組合作為獨立成分重要性的度量指標,以此來解決獨立成分的排序問題。韓語語音庫上的實驗表明,該算法在有效實現(xiàn)模型稀疏化的同時保證了模型識別精度。 (4)針對非支持向量參與模型訓(xùn)練會造成模型復(fù)雜度提高和模型識別性能降低的問題,本文從數(shù)據(jù)挖掘和支持向量的幾何分布含義兩個方面出發(fā),提出了基于支持向量預(yù)選取的最小二乘支持向量機稀疏化算法。該算法在模型訓(xùn)練之前,將K均值聚類算法提取的關(guān)鍵表征樣本和中心距離比值算法選取的邊界樣本的并集作為預(yù)選支持向量,從而有效實現(xiàn)了稀疏化。經(jīng)韓語語音庫和Aurora-2語音庫實驗表明,該方法在幾乎不損失識別精度的基礎(chǔ)上提高了識別速度,達到了稀疏化的目的。
[Abstract]:Speech recognition is the most direct and convenient means of human-computer interaction, which belongs to the category of multidimensional pattern recognition. Least squares support vector machine (LS-SVM) is a hot pattern recognition algorithm in the field of machine learning. As an extension of standard SVM, LS-SVM has small sample learning. It can avoid the "high dimension disaster" and the advantages of simple and easy to implement the model training algorithm, so it is suitable for the recognition of complex speech signals. However, it has the disadvantage of lack of sparse solution, which leads to the increase of model complexity and the reduction of system recognition speed. This paper studies this problem, and the specific research contents are as follows: In this paper, the principle of speech recognition system and least square support vector machine (LS-SVM) is deeply studied, and the LS-SVM is introduced into speech recognition system, which overcomes the need of prior distribution knowledge in traditional speech recognition methods. Artificial neural network is prone to the defect of "overlearning". (2) the importance of model parameters to the learning ability and generalization ability of the system is studied carefully, and the particle swarm optimization algorithm combined with K-fold cross-validation is proposed to optimize the optimal parameters. The complex manual debugging and the time-consuming grid algorithm are avoided. 3) based on the in-depth study of the reasons for the lack of sparsity of LS-SVM and the effect of speech sample feature dimension on the performance of the model, an independent component analysis (ICA) based least-squares SVM thinning method is proposed. The method firstly uses independent component analysis (ICA) to reduce the dimension of speech features, and then, after model training, a fast pruning algorithm based on ICA is used to reduce the kernel matrix. The combination of kurtosis and skewness is used as a measure of the importance of independent components in the process of reduction, so as to solve the problem of sorting independent components. The experiments on the Korean language corpus show that the algorithm not only realizes the sparse model but also ensures the accuracy of model recognition. 4) aiming at the problem that non-support vector participation in model training will lead to higher model complexity and lower model recognition performance, this paper starts from two aspects: data mining and geometric distribution meaning of support vector. A least squares support vector machine thinning algorithm based on support vector preselection is proposed. Before the model training, the union of the key representation samples extracted by the K-means clustering algorithm and the boundary samples selected by the centroid distance ratio algorithm is taken as the pre-selected support vector. The experiments of Korean phonetic corpus and Aurora-2 corpus show that the method improves the recognition speed and achieves the purpose of thinning on the basis of almost no loss of recognition accuracy.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN912.3

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