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Auditory Based Modification of MFCC Feature Extraction for Robust Automatic Speech Recognition

The human auditory perception system is much more noise-robust than any state-of theart
automatic speech recognition (ASR) system. It is expected that the noise-robustness of
speech feature vectors may be improved by employing more human auditory functions in the
feature extraction procedure.
Forward masking is a phenomenon of human auditory perception, that a weaker sound
is masked by the preceding stronger masker. In this work, two human auditory mechanisms,
synaptic adaptation and temporal integration are implemented by filter functions and incorporated
to model forward masking into MFCC feature extraction. A filter optimization algorithm
is proposed to optimize the filter parameters.
The performance of the proposed method is evaluated on Aurora 3 corpus, and the procedure
of training/testing follows the standard setting provided by the Aurora 3 task. The
synaptic adaptation filter achieves relative improvements of 16.6% over the baseline. The
temporal integration and modified temporal integration filter achieve relative improvements
of 21.6% and 22.5% respectively. The combination of synaptic adaptation with each of temporal
integration filters results in further improvements of 26.3% and 25.5%. Applying the
filter optimization improves the synaptic adaptation filter and two temporal integration filters,
results in the 18.4%, 25.2%, 22.6% improvements respectively. The performance of the
combined-filters models are also improved, the relative improvement are 26.9% and 26.3%.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0901109-143419
Date01 September 2009
CreatorsChiou, Sheng-chiuan
ContributorsJeih-weih Hung, Jing-shin Chang, Jeih-weih Hung, Jing-shin Chang, Hsin-min Wang, Chia-ping Chen, Chung-hsien Wu
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0901109-143419
Rightsunrestricted, Copyright information available at source archive

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