Paper Info
Title
Improving the Quality of Automatic Speech Recognition in Trucks.
Abstract
In this paper we consider the problem of the DNN-HMM acoustic models training for automatic speech recognition systems on russian language in modern commercial trucks. The speech database for training and testing the ASR system was recorded in various models of trucks, operating under different conditions. The experiments on the test part of the speech database, show that acoustic models trained on the base of specifically modeled training speech database enable to improve the recognition quality in a moving truck from 35% to 88% compared to the acoustic models trained on a clean speech. Also a new topology of the neural network was proposed. It allows to reduce the computational costs significantly without loss of the recognition accuracy.
Year
DOI
Venue
2016
10.1007/978-3-319-43958-7_43
Lecture Notes in Computer Science
Keywords
Field
DocType
ASR,DNN,MFCC,CMVN,Multi-bottleneck,Database,Trucks
Truck,Mel-frequency cepstrum,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Artificial neural network
Conference
Volume
ISSN
Citations 
9811
0302-9743
0
PageRank 
References 
Authors
0.34
6
3
Name
Order
Citations
PageRank
Maxim Korenevsky1257.15
Ivan Medennikov2256.44
Vadim Shchemelinin3264.56