本文選題：聲紋識別　切入點：特征提取　出處：《廣東工業(yè)大學》2012年碩士論文

【摘要】：近年來,聲紋識別技術在逐漸的成熟,聲紋識別作為一種生物認證技術,有其獨特的優(yōu)點,如聲音是非接觸式的,自然的,用戶容易接受。因為語言這一媒介的優(yōu)勢,通過語音身份認證技術,聲紋識別迅速應用到實際,突出了巨大的市場潛力,聲紋識別技術已成為一個新興的高技術產業(yè)。隨著計算機硬件和軟件技術,半導體技術,電子技術,通信技術和網絡技術的發(fā)展,以及嵌入式技術的不斷發(fā)展和更新,其性能和便攜性大大提高。實時數(shù)據采集,濾波處理,可以在低功耗,體積小的嵌入式設備完成。今天,處理器因為其特殊的結構和高的編譯效率使其能夠快速的實現(xiàn)聲紋識別算法,滿足今天的數(shù)字信號處理和高實時性的要求。高性能嵌入式聲紋識別系統(tǒng)的聲紋識別技術,因為方便,經濟性,準確性和嵌入式系統(tǒng)的便攜性,移動性等優(yōu)點,被廣泛應用于人們的日常生活,擁有廣闊的發(fā)展前景。 本文在分析聲紋識別的相關理論與技術的基礎上,重點研究了基于Mel倒譜系數(shù)(MFCC)的特征參數(shù)的提取和DTW算法進行改進,對一些不足之處進行相應的改進。最后,它被應用在基于ARM11與WinCE嵌入式平臺下實現(xiàn)的一個小容量的嵌入式聲紋識別系統(tǒng)。在前人工作的基礎上,本文改進工作主要包括以下三個方面： 1.特征提取方面：對標準的MFCC中存在的不足,提出了相應的改進,加權差分結合MFCC語音特征參數(shù)。使用短時幀能量和短時加權過零率替代MFCC中有負識別作用的第1,2階分量,并根據語音成分的不同貢獻率進行加權,然后進行一階差分,最終會合并成一個新的特征參數(shù)。 2.DTW算法方面：使用改進的DTW算法,替代標準的DTW算法,采用整體路徑約束,該算法具有很好的魯棒性,從而提高了算法的效率和代碼質量。 3.嵌入式系統(tǒng)實現(xiàn)方面：在基于ok6410的arm11嵌入式系統(tǒng)中的資源相對有限的條件下,進行了一些優(yōu)化處理。包括操作系統(tǒng)的優(yōu)化定制和移植,通過跨平臺的軟件開發(fā),成功在搭建好的嵌入式開發(fā)平臺上實現(xiàn)了聲紋識別系統(tǒng)。并研究分析了改進的DTW算法和傳統(tǒng)DTW算法之間的性能差異,對在嵌入式中的運行情況進行了分析。 該系統(tǒng)相關的實驗,實驗結果表明,對同一文本的內容,識別系統(tǒng)的識別率比較高,對文本無關的內容,識別率應該改進；用改進后的算法和特征參數(shù),系統(tǒng)的平均識別率提高4%左右。
[Abstract]:In recent years, voicerecognition technology has gradually matured. As a biometric authentication technology, voicerecognition has its unique advantages, such as sound is contactless, natural and easy to accept by users, because of the advantage of language as a medium. Through the voice identification technology, voicerecognition is applied to practice rapidly, which highlights the huge market potential. Voicerecognition technology has become a new high-tech industry. With the computer hardware and software technology, semiconductor technology, With the development of electronic technology, communication technology and network technology, as well as the continuous development and update of embedded technology, its performance and portability are greatly improved. Today, because of its special structure and high compilation efficiency, the processor can quickly realize the voiceprint recognition algorithm. To meet the requirements of today's digital signal processing and high real-time. High performance embedded voice recognition system voiceprint recognition technology, because of the advantages of convenience, economy, accuracy and embedded system portability, mobility, and other advantages, Widely used in people's daily life, has a broad development prospects. On the basis of analyzing the theory and technology of voiceprint recognition, this paper focuses on the feature parameter extraction based on Mel cepstrum coefficient and the improvement of DTW algorithm. It is applied to a small capacity embedded voiceprint recognition system based on ARM11 and WinCE embedded platform. Based on the previous work, the improvement work in this paper mainly includes the following three aspects:. 1. Feature extraction: for the shortcomings of standard MFCC, a corresponding improvement is put forward. The weighted difference is combined with MFCC speech feature parameters. The second order component with negative recognition in MFCC is replaced by short-time frame energy and short-time weighted zero-crossing rate. The speech components are weighted according to different contribution rates, and then the first order difference is carried out, which will be merged into a new feature parameter. In the aspect of 2.DTW algorithm, the improved DTW algorithm is used instead of the standard DTW algorithm and the global path constraint is adopted. The algorithm has good robustness and improves the efficiency and code quality of the algorithm. 3. The realization of embedded system: under the condition of limited resources in arm11 embedded system based on ok6410, some optimization processes are carried out, including the optimized customization and transplantation of operating system, and the development of cross-platform software. The voiceprint recognition system is successfully implemented on a well built embedded development platform, and the performance difference between the improved DTW algorithm and the traditional DTW algorithm is analyzed, and the running situation in the embedded system is analyzed. The experimental results show that the recognition rate of the system is high for the content of the same text, the recognition rate should be improved for the text-independent content, and the improved algorithm and feature parameters should be used. The average recognition rate of the system is increased by about 4%.
【學位授予單位】：廣東工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TP368.1;TN912.34

【引證文獻】

相關碩士學位論文 前1條

1 鄒節(jié)凱;基于SOPC技術的噪聲環(huán)境下聲紋識別系統(tǒng)的研究[D];武漢理工大學;2013年

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本文編號：1675992