以多階層向量量化為基礎之語者辨識

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 學位論文 > Item 987654321/35772

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/35772

题名:	以多階層向量量化為基礎之語者辨識
其它题名:	Speaker identification based on multistage vector quantization
作者:	鄭竹勝;Cheng, Chu-sheng
贡献者:	淡江大學電機工程學系碩士班謝景棠;Hsieh, Ching-tang
关键词:	語者辨識;語音切割;向量量化;多階層向量量化;Speaker Identification;Speech Segmentation;Vector Quantization;Multistage VQ
日期:	2007
上传时间:	2010-01-11 07:08:11 (UTC+8)
摘要:	為提高系統的抗雜訊能力，我們提出一個新的語者辨識系統。在辨識器前作ㄧ區分週期與非週期性語料的預處理，因為語音的非週期性語料易受雜訊影響且能量較弱，我們僅利用週期性語料進行語者辨認，又因屬於極零模型的線性預估倒頻譜參數(LPCC) 比梅爾刻度倒頻譜參數(MFCC)對週期性語料有較好的描述能力，我們使用LPCC作為語者辨識的特徵參數，且多階層的VQ比單階層VQ具有更好的編碼能力與需較少的容量，我們使用作為語者辨識的分類器，以提升辨識率。 We presents an effective speaker recognition system for improving the performance in noisy environments and under various recording conditions, including microphone, common phones. In our previous works, we segment speech manually into regions of aperiodic consonant and others. As we find the characteristic of aperiodic consonant of LPCCs effect the performance of speaker identification in noisy environments. For speech feature extraction, we use LPCC and MFCC. The experimental results show that LPCC is more effective than MFCC, particularly extract form periodic corpora. For classifiers, we have tested VQ (Vector quantization) and 2-stage VQ. The experimental results show that 2-stage VQ is more effective than VQ and the 2-stage VQ is computationally more efficient than VQ. In our experiments, to evaluate the combinations of speech features and speaker classifiers, we have used two speech corpora in this study, include TIMIT and NTIMIT database.
显示于类别:	[電機工程學系暨研究所] 學位論文

文件中的档案:

档案	大小	格式	浏览次数
	0Kb	Unknown	293	检视/开启

在機構典藏中所有的数据项都受到原著作权保护.

TAIR相关文章

数据加载中.....