Paper Info
Title
Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge.
Abstract
This paper presents the contribution to the third u0027CHiMEu0027 speech separation and recognition challenge including both front-end signal processing and back-end speech recognition. In the front-end, Multi-channel Wiener filter (MWF) is designed to achieve background noise reduction. Different from traditional MWF, optimized parameter for the tradeoff between noise reduction and target signal distortion is built according to the desired noise reduction level. In the back-end, several techniques are taken advantage to improve the noisy Automatic Speech Recognition (ASR) performance including Deep Neural Network (DNN), Convolutional Neural Network (CNN) and Long short-term memory (LSTM) using medium vocabulary, Lattice rescoring with a big vocabulary language model finite state transducer, and ROVER scheme. Experimental results show the proposed system combining front-end and back-end is effective to improve the ASR performance.
Year
Venue
Field
2015
arXiv: Sound
Wiener filter,Noise reduction,Signal processing,Background noise,Convolutional neural network,Computer science,Speech recognition,Artificial intelligence,Artificial neural network,Distortion,Machine learning,Language model
DocType
Volume
Citations 
Journal
abs/1509.06103
2
PageRank 
References 
Authors
0.39
2
8
Name
Order
Citations
PageRank
Xiaofei Wang1134.99
Chao Wu272.15
Pengyuan Zhang35019.46
Ziteng Wang4175.55
Yong Liu514634.31
Xu Li682.22
Qiang Fu779181.92
Yonghong Yan 000288319.58