復雜背景下聲紋特征提取與識別
發(fā)布時間：2018-11-02 09:31
【摘要】：隨著互聯(lián)網以及信息化的迅速發(fā)展,聲紋識別技術在金融、證券、社保、電子商務、銀行等遠程客戶服務的身份確認和公安、軍隊安全部門的特定人身份自動檢測和認證中具有廣泛的應用價值和前景需求,是當今世界聲音信號處理和生物特征信息檢測與識別領域的重要探索方向。近幾十年來,在這一領域的研究已經取得了重大進展,但因為說話人個性特征易受外界因素影響以及具體實際環(huán)境的復雜多變性,其瓶頸效應也逐漸凸顯,因此,在復雜背景下研究有效的語音信息檢測方法和更具魯棒性的特征提取算法對于提高系統(tǒng)的識別率具有非常重要的意義。 復雜背景下的聲紋識別技術是在高度復雜噪聲情況下,通過檢測出聲音并進一步進行特征提取后,經過分析處理建立識別模型,最后應用識別模型對說話人進行識別。論文主要研究語音端點檢測方法和特征提取方法來提高識別效率,主要工作如下。 首先,在聲音預處理階段,提出了嘈雜環(huán)境下的兩種語音信號端點檢測方法,根據(jù)不同背景復雜程度的信噪比高低分別采用基于譜熵的端點檢測算法和基于短時能量和過零率的雙門限端點檢測算法,實驗表明,背景為高信噪比情況下基于短時能量和過零率的雙門限端點檢測算法效果較好,背景為低信噪比情況下基于譜熵的端點檢測算法較優(yōu)。 其次,在特征提取階段,利用倒譜法計算出基音周期參數(shù),再通過Mel濾波器組將語音信號功率譜轉換成Mel倒譜系數(shù)(MFCC),然后利用改進特征提取算法將兩種參數(shù)組成一種聲紋特征參量,同時分別對它們進行了實驗仿真。 最后,在聲紋識別階段,首先提出帶噪特征的識別算法(SEMG)算法,即在復雜背景下對語音信號利用基于譜熵的端點檢測算法檢測端點后,再利用改進特征提取算法特征提取,最后為每個說話人建立一個高斯混合模型(GMM),并通過實驗驗證了SEMG算法的有效性,達到了理想結果。
[Abstract]:With the rapid development of the Internet and information technology, voiceprint identification technology in finance, securities, social security, e-commerce, banking and other remote customer service identification and public security, The automatic detection and authentication of the specific identity of the military security department has a wide range of application value and foreground requirements. It is an important exploration direction in the field of sound signal processing and biometric information detection and recognition in the world today. In recent decades, great progress has been made in the research in this field. However, because the speaker's personality is easily influenced by the external factors and the complex variability of the actual environment, the bottleneck effect is becoming more and more prominent. It is very important to study the effective speech information detection method and the more robust feature extraction algorithm in complex background for improving the recognition rate of the system. The voiceprint recognition technology in complex background is based on the detection of sound and further feature extraction. After analyzing and processing, the recognition model is established. Finally, the recognition model is used to recognize the speaker. This paper mainly studies the speech endpoint detection method and feature extraction method to improve the recognition efficiency, the main work is as follows. Firstly, in the stage of sound preprocessing, two speech signal endpoint detection methods in noisy environment are proposed. According to the signal-to-noise ratio of different background complexity, the two threshold endpoint detection algorithms based on spectral entropy and short-time energy and zero-crossing rate are used, respectively. The experimental results show that, The dual-threshold endpoint detection algorithm based on short-time energy and zero-crossing rate is better in the case of high signal-to-noise ratio (SNR), and the algorithm based on spectral entropy is better when the background is low SNR. Secondly, in feature extraction stage, pitch period parameters are calculated by cepstrum method, and then the power spectrum of speech signal is converted to Mel cepstrum coefficient (MFCC), by Mel filter bank. Then, the improved feature extraction algorithm is used to make two parameters into one voiceprint feature parameter, and at the same time, the experimental simulation of them is carried out. Finally, in the stage of voiceprint recognition, a noisy feature recognition algorithm (SEMG) is proposed, that is, the speech signal is detected by spectral entropy based endpoint detection algorithm under complex background, and then the improved feature extraction algorithm is used to extract features. Finally, a Gao Si hybrid model, (GMM), is established for each speaker, and the effectiveness of the SEMG algorithm is verified by experiments, and the ideal results are obtained.
【學位授予單位】：中南林業(yè)科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN912.34
本文編號：2305639
[Abstract]:With the rapid development of the Internet and information technology, voiceprint identification technology in finance, securities, social security, e-commerce, banking and other remote customer service identification and public security, The automatic detection and authentication of the specific identity of the military security department has a wide range of application value and foreground requirements. It is an important exploration direction in the field of sound signal processing and biometric information detection and recognition in the world today. In recent decades, great progress has been made in the research in this field. However, because the speaker's personality is easily influenced by the external factors and the complex variability of the actual environment, the bottleneck effect is becoming more and more prominent. It is very important to study the effective speech information detection method and the more robust feature extraction algorithm in complex background for improving the recognition rate of the system. The voiceprint recognition technology in complex background is based on the detection of sound and further feature extraction. After analyzing and processing, the recognition model is established. Finally, the recognition model is used to recognize the speaker. This paper mainly studies the speech endpoint detection method and feature extraction method to improve the recognition efficiency, the main work is as follows. Firstly, in the stage of sound preprocessing, two speech signal endpoint detection methods in noisy environment are proposed. According to the signal-to-noise ratio of different background complexity, the two threshold endpoint detection algorithms based on spectral entropy and short-time energy and zero-crossing rate are used, respectively. The experimental results show that, The dual-threshold endpoint detection algorithm based on short-time energy and zero-crossing rate is better in the case of high signal-to-noise ratio (SNR), and the algorithm based on spectral entropy is better when the background is low SNR. Secondly, in feature extraction stage, pitch period parameters are calculated by cepstrum method, and then the power spectrum of speech signal is converted to Mel cepstrum coefficient (MFCC), by Mel filter bank. Then, the improved feature extraction algorithm is used to make two parameters into one voiceprint feature parameter, and at the same time, the experimental simulation of them is carried out. Finally, in the stage of voiceprint recognition, a noisy feature recognition algorithm (SEMG) is proposed, that is, the speech signal is detected by spectral entropy based endpoint detection algorithm under complex background, and then the improved feature extraction algorithm is used to extract features. Finally, a Gao Si hybrid model, (GMM), is established for each speaker, and the effectiveness of the SEMG algorithm is verified by experiments, and the ideal results are obtained.
【學位授予單位】：中南林業(yè)科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN912.34
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