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 LFbased 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.

本文讨论了基于本地特征方法的神经网络模型在孟加拉语音识别中的应用，通过输入语音信号并从中提取本地特征，利用多层神经网络提取音素矩阵，然后对音素进行分类，实现更高效、更准确的自动语音识别系统。本系统相较于基于MFCC的系统具有更高的音素识别率且需要更少的HMM混合成分。

局部特征或梅尔频率倒谱系数——对于基于MLN的孟加拉语语音识别哪种更好？