Local Feature or Mel Frequency Cepstral Coefficients - Which One Is Better for MLN-Based Bangla Speech Recognition?

被引：0
|
作者：
Hassan, Foyzul [1 ]
Kotwal, Mohammed Rokibul Alam [1 ]
Rahman, Md Mostafizur [1 ]
Nasiruddin, Mohammad [2 ]
Latif, Md Abdul [2 ]
Huda, Mohammad Nurul [1 ]
机构：
[1] United Int Univ, Dhaka, Bangladesh
[2] Univ Asia Pacific, Dhaka, Bangladesh
来源：
ADVANCES IN COMPUTING AND COMMUNICATIONS, PT 2 | 2011年 / 191卷
关键词：
Local Feature; Mel Frequency Cepstral Coefficient; Multilayer Neural Network; Hidden Markov Model; Automatic Speech Recognition;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper discusses the dominancy of local features (LFs), as input to the multilayer neural network (MLN), extracted from a Bangla input speech over mel frequency cepstral coefficients (MFCCs). Here, LF-based method comprises three stages: (i) LF extraction from input speech, (ii) phoneme probabilities extraction using MLN from LF and (iii) the hidden Markov model (HMM) based classifier to obtain more accurate phoneme strings. In the experiments on Bangla speech corpus prepared by us, it is observed that the LF-based automatic speech recognition (ASR) system provides higher phoneme correct rate than the MFCC-based system. Moreover, the proposed system requires fewer mixture components in the HMMs.
引用
收藏
页码：154 / +
页数：2
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