English Spoken Digits Database under noise conditions for research: SDDN

Ouisaadane, A.; Safi, Saïd; Frikel, Miloud

doi:10.1109/wits.2019.8723698

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…The researches for the Arabic language speech recognition is still weak, especially the continuous speech recognition in a noisy environment [25][26][27][28][29]. However, all applications in speech recognition are mostly available in English, like the works presented in [30][31][32][33][34]. Despite Arabic being the fourth most widely spoken language of the world, which is why our research was focused on Arabic speech recognition in noisy environments and because it is the first work using the methods that we mentioned earlier.…”

Section: Introductionmentioning

confidence: 99%

Arabic digits speech recognition and speaker identification in noisy environment using a hybrid model of VQ and GMM

2020

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This paper presents an automatic speaker identification and speech recognition for Arabic digits in noisy environment. In this work, the proposed system is able to identify the speaker after saving his voice in the database and adding noise. The mel frequency cepstral coefficients (MFCC) is the best approach used in building a program in the Matlab platform; also, the quantization is used for generating the codebooks. The Gaussian mixture modelling (GMM) algorithms are used to generate template, feature-matching purpose. In this paper, we have proposed a system based on MFCC-GMM and MFCC-VQ approaches on the one hand and by using the hybrid approach MFCC-VQ-GMM on the other hand for speaker modeling. The white Gaussian noise is added to the clean speech at several signal-to-noise ratio (SNR) levels to test the system in a noisy environment. The proposed system gives good results in recognition rate.

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