Histogram equalization for noise robust speech recognition
No Thumbnail Available
URL
Journal Title
Journal ISSN
Volume Title
Helsinki University of Technology |
Diplomityö
Checking the digitized thesis and permission for publishing
Instructions for the author
Instructions for the author
Authors
Date
2009
Department
Major/Subject
Informaatiotekniikka
Mcode
T-61
Degree programme
Tietotekniikan tutkinto-ohjelma
Language
en
Pages
(5+) 50
Series
Abstract
Automatic speech recognition (ASR) is a fascinating field of science where the machine almost becomes human. Being able to communicate naturally with a machine has been a dream for a long time. Today, the technology makes it possible for the machine to understand human speech. However the quality of recognition suffers a lot from surrounding noise, and noisy environments are our everyday life conditions. This work presents a technique to improve noise robustness of ASR systems based on histogram equalization. This method has been proven efficient in the field of image processing and here we show that it can he successfully applied to audio data too. The idea behind it is to equalize noisy data and make it "sound like" clean data so that ASR systems trained on clean speech can recognize noisy speech more accurately. Experiments are conducted on Helsinki University of Technology's ASR system, and show a significant improvement in large vocabulary continuous speech recognition of noisy data on Aurora 4 database.Description
Supervisor
Oja, ErkkiThesis advisor
Kurimo, MikkoKeywords
speech recognition, histogram equalization, noise robustness