Open Access Open Access  Restricted Access Subscription or Fee Access

Autocorrelation-domain method for noise robust speech recognition

Hajer Rahali, Zied Hajaiej, Noureddine Ellouze

Abstract


The goal of robust feature extraction is to improve the performance of speech recognition in adverse conditions. This paper introduces a novel representation of speech for the cases where the speech signal is corrupted by additive noises. In this method, the speech features are computed by reducing additive noise effects via filtering stage that is based on an adaptive filtering in the spectral domain followed by filtering with gammachirp filter. A task of isolated word recognition was used to demonstrate the efficiency of these robust features. To improve the robustness of speech we introduce, in this paper, a new set of PLP vector. The above-mentioned technique was tested with white noise and colored noise such as airport, exhibition and babble noises under various noisy conditions within TIMIT and AURORA database. Experimental results show significant improvement in comparison to the results obtained using traditional features extraction techniques.

Keywords


Gammachirp filter, PLP, noisy speech, HMM\GMM, autocorrelation-domain.

Full Text:

PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information. Also: DOI is paid service which provided by a third party. We never mentioned that we go for this for our any journal. However, journal have no objection if author go directly for this paid DOI service.