本文選題：聲紋識(shí)別　切入點(diǎn)：特征提取　出處：《哈爾濱理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：聲紋如同指紋、人臉一樣，是人體特有的一種生物特征。由于其方便性和經(jīng)濟(jì)性，聲紋識(shí)別作為生物認(rèn)證技術(shù)的一種，逐漸走入大眾的視線。聲紋識(shí)別不同于語(yǔ)音識(shí)別，顧名思義，聲紋識(shí)別注重的是待識(shí)別的語(yǔ)音信號(hào)中說(shuō)話人的聲紋特征，無(wú)需知道說(shuō)話內(nèi)容。由于每個(gè)人都有其獨(dú)一無(wú)二的聲紋特征，很難被模仿偽造，因此，相對(duì)于其他生物認(rèn)證技術(shù)，聲紋識(shí)別技術(shù)在身份認(rèn)證領(lǐng)域更加地安全、可靠。 本文主要是對(duì)與文本無(wú)關(guān)的聲紋識(shí)別系統(tǒng)的關(guān)鍵技術(shù)進(jìn)行研究，力圖在前人研究的基礎(chǔ)上有所創(chuàng)新，以提高系統(tǒng)的識(shí)別率。首先在宏觀上分析了聲紋識(shí)別的課題背景、發(fā)展現(xiàn)狀及研究難點(diǎn)等，并對(duì)聲紋識(shí)別系統(tǒng)的結(jié)構(gòu)原理進(jìn)行介紹，其次分析了聲紋識(shí)別系統(tǒng)的端點(diǎn)檢測(cè)部分，，然后重點(diǎn)對(duì)聲紋識(shí)別的關(guān)鍵部分特征提取模塊進(jìn)行研究。主要包括分析聲紋識(shí)別系統(tǒng)的特征提取模塊中線性預(yù)測(cè)倒譜系數(shù)(Linear Prediction Cepstrum Coefficient，LPCC)與Mel頻率倒譜系數(shù)(Mel Frequency Cepstrum Coefficient，MFCC)的提取原理，并對(duì)MFCC參數(shù)的提取進(jìn)行了改進(jìn)，提出了基于小波變換和改進(jìn)的MFCC參數(shù)組合特征的提取算法。最后用高斯混合模型通過(guò)實(shí)驗(yàn)的方式對(duì)特征提取部分的不同算法進(jìn)行分析比較，達(dá)到了提高了系統(tǒng)的效率的目的。
[Abstract]:Sound pattern, like fingerprint and face, is a special biological feature of human body. Because of its convenience and economy, sound pattern recognition, as a kind of biometric authentication technology, has gradually come into the public's sight. Voice pattern recognition is different from speech recognition. As the name implies, voice-pattern recognition focuses on the speaker's voice-pattern feature in the speech signal to be recognized, without knowing what to say. Because everyone has its unique voice-pattern feature, it is difficult to imitate and forge, so, Compared with other biometric authentication technologies, voiceprint recognition technology is more secure and reliable in the field of identity authentication. This paper mainly studies the key technology of text-independent voiceprint recognition system, and tries to innovate on the basis of previous research in order to improve the recognition rate of the system. This paper introduces the structure principle of the voiceprint recognition system, and then analyzes the endpoint detection part of the voiceprint recognition system. Then the key part of feature extraction module of sound stripe recognition is studied, including the principle of linear predictive cepstrum coefficient (linear Prediction Cepstrum coefficient) and Mel frequency cepstrum coefficient (Mel Frequency Cepstrum efficient Prediction) in the feature extraction module of sound stripe recognition system, and the principle of linear predictive cepstrum coefficient (LPC) and Mel frequency cepstrum coefficient (Mel Frequency Cepstrum efficient Prediction) are analyzed. After improving the extraction of MFCC parameters, an algorithm based on wavelet transform and improved MFCC parameter combination feature extraction is proposed. Finally, the different algorithms of feature extraction part are analyzed and compared by using Gao Si mixed model through experiments. The efficiency of the system is improved.
【學(xué)位授予單位】：哈爾濱理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN912.34

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