Paper Info
Title
Detection of spoken words in noise: Comparison of human performance to maximum likelihood detection
Abstract
In this work, we are interested in assessing the optimality of the human auditory system, when the input stimuli is natural speech that is affected by additive noise. In order to do this, we consider the DANTALE II listening test paradigm of Wagener et al., which has been used to evaluate the intelligibility of noisy speech by exposing human listeners to a selection of constructed noisy sentences. Inspired by this test, we propose a simple model for the communication and classification of noisy speech that takes place in the test. We then identify a number of key properties that the test subjects satisfy, and combine these with our proposed model in order to derive optimal classifiers in the sense of maximum a posteriori estimation. We finally compare the performance of the classifiers to that of humans on the same noisy test sentences. The results reveal that at low SNRs, the human performance is inferior to that of the optimal classifiers. We conclude that in this special task, the human auditory system is not optimal.
Year
DOI
Venue
2016
10.1109/GlobalSIP.2016.7905800
2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Keywords
Field
DocType
DANTALE II listening test,maximum likelihood classifier,optimal classifier,human performance
Noise measurement,Maximum likelihood detection,Pattern recognition,Computer science,Signal-to-noise ratio,Auditory system,Speech recognition,Artificial intelligence,Maximum a posteriori estimation,Stimulus (physiology),Human auditory system,Intelligibility (communication)
Conference
ISSN
ISBN
Citations 
2376-4066
978-1-5090-4546-4
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Mohsen Zareian Jahromi101.01
Jan Østergaard220128.38
Jesper Jensen31548133.47