@inproceedings{2a699970e7b1404685adef2711b598d0,
title = "Robust speech recognition combining cepstral and articulatory features",
abstract = "In this paper, a nonlinear relationship between pronunciation and auditory perception is introduced into speech recognition, and superior robustness is shown in the results. The Extreme Learning Machine mapping the relations was trained with Mocha-TIMIT database. Articulatory Features (AFs) were obtained by the network and MFCCs were fused for training acoustic model-DNN-HMM and GMM-HMM in this experiment. It has an 117.0% relative increment of WER with MFCCs-AFs-GMM-HMM while 125.6% with MFCCs-GMM-HMM And the performance of the model DNN-HMM is better than that of the model GMM-HMM, both with relative and absolute performance.",
keywords = "DNN, ELM, articulatory features, robustness, speech recognition",
author = "Zha, {Zhuan Ling} and Jin Hu and Zhan, {Qing Ran} and Shan, {Ya Hui} and Xiang Xie and Jing Wang and Cheng, {Hao Bo}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 3rd IEEE International Conference on Computer and Communications, ICCC 2017 ; Conference date: 13-12-2017 Through 16-12-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/CompComm.2017.8322773",
language = "English",
series = "2017 3rd IEEE International Conference on Computer and Communications, ICCC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1401--1405",
booktitle = "2017 3rd IEEE International Conference on Computer and Communications, ICCC 2017",
address = "United States",
}