本文選題：語音識別　切入點：非特定人　出處：《安徽工業(yè)大學》2016年碩士論文

【摘要】：在科學技術發(fā)展的推動下,語音識別技術已經逐漸從研究階段進入到實際應用階段。但是,對非特定人的語音識別研究仍在激烈的探討中,怎樣提高該系統(tǒng)的識別率,依舊是當前研究的熱點問題。本文系統(tǒng)地研究了語音識別系統(tǒng)的各個組成部分,針對部分關鍵技術提出了改進的算法,并在MATLAB上建立了相應的非特定人識別系統(tǒng)。文中深入研究了語音識別系統(tǒng)的原理組成部分,包括語音信號的預處理、起止端點的檢測、特征參數的提取。在此基礎上,對三種常用的語音識別方法:動態(tài)時間規(guī)整(DTW)、隱馬爾科夫模型(HMM)與神經網絡模型(ANN)進行了對比分析,并重點研究了隱馬爾科夫模型算法,對該算法中存在的數據溢出問題采取了有效的解決措施。接著,針對低信噪比噪聲環(huán)境下,語音信號的濾波和端點檢測這兩個關鍵技術,分別提出了改進的算法,即:基于經驗模式分解(EMD)和奇異值分解(SVD)差熵法的濾波算法,以及改進的希爾伯特黃變換(HHT)語音端點檢測算法,并將改進后的算法分別與傳統(tǒng)算法的處理結果進行了分析比較。本文在MATLAB平臺上建立了基于HMM模型的非特定人的語音識別系統(tǒng)。結果表明,與傳統(tǒng)的濾波方法以及端點檢測方法相比,改進后的算法提高了識別系統(tǒng)的識別率,充分體現了改進算法的有效性和可行性。最后設計了一個語音識別系統(tǒng)GUI界面,包括語音信號處理的界面和語音的識別過程界面,對語音庫中的語音進行實時識別實驗,驗證了所用系列方法的有效性。
[Abstract]:With the development of science and technology, speech recognition technology has gradually moved from the research stage to the practical application stage. However, the research on the speech recognition of non-specific people is still under intense discussion, how to improve the recognition rate of the system, It is still a hot topic in current research. In this paper, the components of speech recognition system are systematically studied, and an improved algorithm is proposed for some key technologies. In this paper, the principle of speech recognition system is deeply studied, including speech signal preprocessing, endpoint detection and feature parameter extraction. Three common speech recognition methods: dynamic time warping (DTW), Hidden Markov Model (HMMM) and Neural Network (Ann) are compared and analyzed. This paper takes effective measures to solve the problem of data overflow in the algorithm. Then, aiming at the two key technologies of speech signal filtering and endpoint detection in low signal-to-noise noise environment, the improved algorithm is proposed respectively. That is, the filtering algorithm based on empirical mode decomposition (EMD) and singular value decomposition (SVD) differential entropy method, and the improved Hilbert Huang transform (HHT) speech endpoint detection algorithm. The improved algorithm is analyzed and compared with the results of the traditional algorithm. In this paper, a speech recognition system based on the HMM model is established on the MATLAB platform. The results show that, Compared with the traditional filtering method and the endpoint detection method, the improved algorithm improves the recognition rate of the recognition system, and fully reflects the effectiveness and feasibility of the improved algorithm. Finally, a speech recognition system GUI interface is designed. It includes the interface of speech signal processing and the interface of speech recognition process. The experiments of speech recognition in speech database are carried out in real time, and the validity of the series of methods is verified.
【學位授予單位】：安徽工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TN912.34

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本文編號：1669740