【摘要】：近年來,在生物特征識(shí)別領(lǐng)域,說話人識(shí)別以其獨(dú)特的安全性、經(jīng)濟(jì)性和準(zhǔn)確性等優(yōu)勢受到越來越多的關(guān)注,并逐漸成為人們生活和工作中重要的身份驗(yàn)證方式,具有廣闊的市場前景。說話人識(shí)別的一個(gè)重要研究分支是說話人確認(rèn),本文著重對說話人確認(rèn)展開研究。本文從說話人確認(rèn)的系統(tǒng)框架入手,對系統(tǒng)的各部分予以詳細(xì)的介紹。隨后針對復(fù)雜條件下的說話人確認(rèn)問題,重點(diǎn)研究了特征提取、說話人分割、模型建立等技術(shù)。本文的主要研究工作及創(chuàng)新點(diǎn)如下:1.構(gòu)建基于GMM-UBM的說話人確認(rèn)系統(tǒng)并將其作為本文的基線系統(tǒng),研究分析了影響系統(tǒng)性能的相關(guān)因素,包括高斯混合度、訓(xùn)練語音長度、得分規(guī)整技術(shù),并通過實(shí)驗(yàn)進(jìn)行驗(yàn)證。2.在特征提取方面,為了提升噪聲環(huán)境下說話人確認(rèn)系統(tǒng)的性能,本文提出了一種具有較強(qiáng)噪聲魯棒性的多窗譜減MFCC特征。多窗譜減MFCC是在已有多窗譜MFCC(Multitaper MFCC)基礎(chǔ)上的改進(jìn),主要是將多窗譜估計(jì)技術(shù)與譜減法進(jìn)行了結(jié)合。仿真結(jié)果表明,當(dāng)測試語音中含有加性噪聲時(shí),與多窗譜MFCC提取算法相比,采用多窗譜減MFCC的說話人確認(rèn)系統(tǒng)性能在等錯(cuò)誤率EER和最小檢測代價(jià)函數(shù)值minDCF兩項(xiàng)評測指標(biāo)上都取得了較好的結(jié)果。3.在說話人分割方面,針對傳統(tǒng)基于BIC的說話人分割算法累積計(jì)算量大、冗余分割點(diǎn)過多,導(dǎo)致分割速度慢、分割準(zhǔn)確度降低的問題,相關(guān)文獻(xiàn)采用了分治算法對其進(jìn)行改進(jìn),雖然改進(jìn)法能夠大幅提高分割速度,但準(zhǔn)確度卻有所降低。為了達(dá)到分割速度與分割準(zhǔn)確度同時(shí)提高的目的,本文首先在具體實(shí)現(xiàn)BIC說話人分割算法時(shí)提出了三步分割的策略,在此基礎(chǔ)上引入分治算法思想對其進(jìn)行改進(jìn)。實(shí)驗(yàn)結(jié)果表明,改進(jìn)后的分割算法在分割速度上有較大提高,準(zhǔn)確度上也有一定提升。4.在模型建立方面,探索研究了i-vector說話人建模技術(shù),重點(diǎn)研究了i-vector的提取過程,構(gòu)建基于i-vector的說話人確認(rèn)系統(tǒng),并將其與基于GMM-UBM的說話人確認(rèn)系統(tǒng)進(jìn)行了對比分析。
[Abstract]:In recent years, in the field of biometrics, speaker recognition has attracted more and more attention because of its unique advantages of security, economy and accuracy, and has gradually become an important way of identity verification in people's lives and work. It has broad market prospects. This paper begins with the system framework of speaker verification, and then introduces each part of the system in detail. Then, aiming at the speaker verification under complex conditions, it focuses on feature extraction, speaker segmentation, model building and other technologies. The main research work and innovation of this paper are as follows: 1. Based on the GMM-UBM speaker verification system as the baseline system of this paper, the related factors affecting the performance of the system are studied and analyzed, including Gaussian mixture, training speech length, scoring regularization technology, and verified by experiments. 2. In feature extraction, in order to improve the performance of speaker verification system in noisy environment, this paper proposes a method to improve the performance of the system. A multi-window spectral subtraction MFCC feature with strong noise robustness is proposed. The multi-window spectral subtraction MFCC is an improvement on the existing multi-window spectral MFCC (Multitaper MFCC), which combines the multi-window spectral estimation technique with the spectral subtraction method. The simulation results show that when the test speech contains additive noise, it is better than the multi-window spectral MFCC extraction algorithm. The speaker verification system using multi-window spectral subtraction MFCC achieves good results in EER with equal error rate and minDCF with minimum detection cost function. In order to improve the segmentation speed and accuracy at the same time, this paper first proposes a three-step segmentation strategy to implement the BIC speaker segmentation algorithm. The experimental results show that the improved segmentation algorithm has a great improvement in segmentation speed and accuracy. 4. In the aspect of model building, I-vector speaker modeling technology is explored and studied, especially the extraction process of I-vector and the construction of I-vector based speaker. The speaker recognition system is analyzed and compared with the speaker verification system based on GMM-UBM.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN912.34

【共引文獻(xiàn)】

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本文編號(hào)：2206917