本文選題：說話人識別　切入點：深度學(xué)習(xí)　出處：《大連理工大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：說話人識別通常稱為聲紋識別,是一種身份認證技術(shù)。它具有用戶接受度高、所需設(shè)備成本低、可擴展性好以及便于移植等優(yōu)勢,可廣泛應(yīng)用于國防軍事、銀行系統(tǒng)、通信、互聯(lián)網(wǎng)、公安司法等領(lǐng)域。說話人識別技術(shù)已經(jīng)取得重要進展,并有產(chǎn)品問世,但尚有許多問題有待深入研究。 深度學(xué)習(xí)是近年來發(fā)展起來的一種神經(jīng)網(wǎng)絡(luò)模型,它具有克服學(xué)習(xí)不充分、深度不足等特點,可用于模式分類、目標跟蹤等領(lǐng)域。本文將深度學(xué)習(xí)理論用于說話人識別中,從基于深度學(xué)習(xí)的說話人識別系統(tǒng)、改進特征的說話人識別算法、改進統(tǒng)計準則的說話人識別算法三個方面,對說話人識別技術(shù)進行了研究,主要工作如下： (1)基于深度學(xué)習(xí)的說話人識別系統(tǒng)的性能研究。將深度學(xué)習(xí)理論引入到說話人識別系統(tǒng)中,在此基礎(chǔ)上分析了測試語音不同單位長度對說話人識別率的影響；在相同測試條件下,不同語音特征參數(shù)對說話人識別準確性的影響；在相同條件下,不同的深度學(xué)習(xí)層數(shù)以及層上節(jié)點數(shù)對于系統(tǒng)識別率的影響,證明了深度學(xué)習(xí)在說話人識別系統(tǒng)中應(yīng)用的正確性與可靠性。 (2)基于改進特征的說話人識別算法。本文將模擬人耳聽覺特性的MFCC與GFCC語音特征參數(shù)結(jié)合起來,組成語音特征向量,并應(yīng)用于說話人識別系統(tǒng)中,提高了系統(tǒng)識別率。 (3)基于改進統(tǒng)計準則的說話人識別算法�？紤]到傳統(tǒng)的系統(tǒng)統(tǒng)計識別算法對于多個說話人識別時存在潛在的誤判,本文應(yīng)用分幀概率打分的統(tǒng)計準則,并進行了說話人識別實驗。實驗仿真驗證了改進統(tǒng)計準則的可行性與有效性。
[Abstract]:Speaker recognition is usually called voiceprint recognition, which is a kind of identity authentication technology. It has the advantages of high user acceptance, low equipment cost, good expansibility and easy to transplant. It can be widely used in defense, military, banking system, communication, etc. The technology of speaker recognition has made important progress in the fields of Internet, public security and judicature, and some products have been produced, but there are still many problems to be studied deeply. Depth learning is a kind of neural network model developed in recent years. It can be used in pattern classification, target tracking and other fields such as pattern classification, target tracking and so on. In this paper, the speaker recognition technology is studied from three aspects: the speaker recognition system based on in-depth learning, the improved speaker recognition algorithm based on improved features, and the speaker recognition algorithm based on improved statistical criteria. The main work is as follows:. 1) Research on the performance of speaker recognition system based on deep learning. The depth learning theory is introduced into speaker recognition system, and the influence of different unit length of speech on speaker recognition rate is analyzed. Under the same test conditions, the influence of different speech feature parameters on the speaker recognition accuracy, and the effect of different depth learning layers and the number of upper segment points on the recognition rate of the system under the same conditions, The correctness and reliability of depth learning in speaker recognition system are proved. In this paper, we combine MFCC with GFCC speech feature parameters to form speech feature vector, and apply it to speaker recognition system to improve the recognition rate. (3) A speaker recognition algorithm based on improved statistical criteria. Considering the potential misjudgment of traditional statistical recognition algorithm for multiple speakers, this paper applies the statistical criterion of framing probability scoring. A speaker recognition experiment is carried out and the simulation results show that the improved statistical criterion is feasible and effective.
【學(xué)位授予單位】：大連理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN912.3

【引證文獻】

相關(guān)碩士學(xué)位論文 前1條

1 呂超;聲源辨別及定位的并行化方法的研究與實現(xiàn)[D];江蘇科技大學(xué);2016年

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本文編號：1641354