@inproceedings{60782a11c0c547dd802e9acfe5be3631,
title = "Fast-LSTM acoustic model for distant speech recognition",
abstract = "The distant-talking automatic speech recognition (ASR) currently becomes an important task in a speech recognition area. Traditionally, hybrid Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) approach are used for ASR. This paper will discuss some deep neural network (DNN) techniques for acoustic modeling, as well as lattice rescoring techniques for ASR. The proposed Fast-long short-term memory neural network (Fast-LSTM) acoustic model combines the time delay neural network (TDNN) and LSTM network to reduce the training time of the standard LSTM acoustic model.",
keywords = "Speech recognition, long short-term memory, time delay neural networks",
author = "Rezki Trianto and Tai, {Tzu Chiang} and Wang, {Jia Ching}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Consumer Electronics, ICCE 2018 ; Conference date: 12-01-2018 Through 14-01-2018",
year = "2018",
month = mar,
day = "26",
doi = "10.1109/ICCE.2018.8326195",
language = "???core.languages.en_GB???",
series = "2018 IEEE International Conference on Consumer Electronics, ICCE 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--4",
editor = "Mohanty, {Saraju P.} and Peter Corcoran and Hai Li and Anirban Sengupta and Jong-Hyouk Lee",
booktitle = "2018 IEEE International Conference on Consumer Electronics, ICCE 2018",
}