DocumentCode
235033
Title
Analysis of Hybrid Feature Research Based on Extraction LPCC and MFCC
Author
Zhu Jianchen ; Liu Zengli
Author_Institution
Inst. of Inf. Eng. & Aeration, Kunming Univ. of Sci. & Technol., Kunming, China
fYear
2014
fDate
15-16 Nov. 2014
Firstpage
732
Lastpage
735
Abstract
The MFCC and LPCC is the speaker recognition feature parameters to be used, the speech of analysis often use these feature, therefore, we need a parameter extraction algorithm and the principle of MFCC and LPCC. The reason is that w e need to understand the technological process. We are modeling to the speaker, the traditional vector quantization model increase the differential equation. The formation of a series of consecutive words distribution vector quantization model, at the same time, extracting the characteristic parameters of Mel frequency cepstrum coefficient and differential speaker that consists of linear prediction cepstrum coefficient combination, we conduct a text about dependent speaker recognition. The algorithm show mixing MFCC and LPCC extraction, based on keeping the response time of the system did not significantly increase the rate the model has been improved to be a certain extent.
Keywords
differential equations; feature extraction; speaker recognition; vector quantisation; LPCC extraction; MFCC extraction; Mel frequency cepstrum coefficient; characteristic parameter extraction; consecutive words distribution; dependent speaker recognition; differential equation; differential speaker; hybrid feature research; linear prediction cepstrum coefficient combination; parameter extraction algorithm; response time; speaker recognition feature parameters; vector quantization model; Band-pass filters; Feature extraction; Filtering theory; Mel frequency cepstral coefficient; Speaker recognition; Speech; Speech recognition; Linear Predictive Cepstral; speaker recongnition;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Intelligence and Security (CIS), 2014 Tenth International Conference on
Conference_Location
Kunming
Print_ISBN
978-1-4799-7433-7
Type
conf
DOI
10.1109/CIS.2014.19
Filename
7016995
Link To Document