论文标题
非母语英语的单词识别的频率中心功能
Frequency-centroid features for word recognition of non-native English speakers
论文作者
论文摘要
这项工作的目的是研究互补的特征，这些特征可以帮助典型的MEL频率sepstral系数（MFCC）在封闭的，有限的set set Word识别的任务中，对不同母亲的非本地英语说话者。与源自语音信号的频谱能量的MFCC不同，提议的频率中心（FCS）封装了语音光谱不同带的光谱中心，由Mel FilterBank定义。观察到这些功能与MFCC结合使用，可以提供英语单词识别的相对性能提高，尤其是在各种嘈杂条件下。两阶段的卷积神经网络（CNN）用于模拟用阿拉伯语，法语和西班牙口音说出的英语单词的特征。
The objective of this work is to investigate complementary features which can aid the quintessential Mel frequency cepstral coefficients (MFCCs) in the task of closed, limited set word recognition for non-native English speakers of different mother-tongues. Unlike the MFCCs, which are derived from the spectral energy of the speech signal, the proposed frequency-centroids (FCs) encapsulate the spectral centres of the different bands of the speech spectrum, with the bands defined by the Mel filterbank. These features, in combination with the MFCCs, are observed to provide relative performance improvement in English word recognition, particularly under varied noisy conditions. A two-stage Convolution Neural Network (CNN) is used to model the features of the English words uttered with Arabic, French and Spanish accents.
