An efficient speech recognition system in adverse conditions using the nonparametric regression

Amrouche, Abderrahmane; Debyeche, Mohamed; Taleb‐Ahmed, Abdelmalik; Rouvaen, J.M.; Yagoub, M.C.E.

doi:10.1016/j.engappai.2009.09.006

Cited by 19 publications

(8 citation statements)

References 13 publications

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“…The speech database used in this work is a part of the database ARADIGITS [13]. It consists of a set of 10 digits of the Arabic language (zero to nine) spoken by 60 speakers of both genders with three repetitions for each digit.…”

Section: Experimental Protocol and Data Collectionmentioning

confidence: 99%

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Performances Evaluation of GMM-UBM and GMM-SVM for Speaker Recognition in Realistic World

Asbai

Amrouche

Debyeche

2011

Lecture Notes in Computer Science

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Abstract. In this paper, an automatic speaker recognition system for realistic environments is presented. In fact, most of the existing speaker recognition methods, which have shown to be highly efficient under noise free conditions, fail drastically in noisy environments. In this work, features vectors, constituted by the Mel Frequency Cepstral Coefficients (MFCC) extracted from the speech signal are used to train the Support Vector Machines (SVM) and Gaussian mixture model (GMM). To reduce the effect of noisy environments the cepstral mean subtraction (CMS) are applied on the MFCC. For both, GMM-UBM and GMM-SVM systems, 2048-mixture UBM is used. The recognition phase was tested with Arabic speakers at different Signal-to-Noise Ratio (SNR) and under three noisy conditions issued from NOISEX-92 data base. The experimental results showed that the use of appropriate kernel functions with SVM improved the global performance of the speaker recognition in noisy environments.

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Section: Experimental Protocol and Data Collectionmentioning

confidence: 99%

“…In a high-dimensional space, the two classes are easier to separate with a hyperplane. To calculate the classification function class (x) we use the dot product in feature space that can also be expressed in the input space by the kernel [13]. Among the most widely used cores we find:…”

Section: Support Vector Machines (Svm)mentioning

confidence: 99%

Performances Evaluation of GMM-UBM and GMM-SVM for Speaker Recognition in Realistic World

Asbai

Amrouche

Debyeche

2011

Lecture Notes in Computer Science

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“…Even though significant advancements in speech recognition technology motivated smart devices's ability to correctly recognize continuous human speech, it is still a challenge to recognize the natural or conversational voice in adverse environments (Ting et al, 2013;Amrouche et al, 2010). In particular, most speech recognition systems tend to demonstrate different performance among speakers, only working well with speakers adhering to the characteristics of the acoustic model (e.g.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised rapid speaker adaptation based on selective eigenvoice merging for user-specific voice interaction

Choi

Park

2015

Engineering Applications of Artificial Intelligence

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“…Many efforts have been invested in improving the mathematical and statistical models used in this field [3,4,5]. Most current systems, however, use features directly borrowed from the technology of speech recognition [6,7,8], based on the search of parameters that minimize the intra-speaker variability and maximizing the inter-speaker variations. These systems are therefore still extremely sensitive to transient changes in the speaker's voice related to temporary changes of his internal state (fatigue, emotions, health).…”

Section: Introductionmentioning

confidence: 99%

Improving Speaker Verification Robustness by Front-End Diversity and Score Level Fusion

Asbai¹,

Bengherabi

Amrouche³

et al. 2013

2013 International Conference on Signal-Image Technology &Amp; Internet-Based Systems

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In this paper, we studied the impact of the mismatch existing between training and testing data due to the presence of an additive noise on the performance of speaker verification system. Using a GMM-UBM system with MAP adaptation as a baseline system, front-end diversity is achieved by using MFCCs and different asymmetric MFCCs stand-alone as features or followed by PCA and LDA as dimensionality reduction techniques applied before the GMM-UBM back-end classifier. A score level fusion framework based on logistic regression is proposed to improve performance and to mitigate noise degradation. The obtained results on both clean and corrupted TIMIT database confirm the superiority of fused system in clean and noisy environment against each system alone, and the drastic degradation of the performances of PCA and LDA based systems in the presence of environmental noise.

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An efficient speech recognition system in adverse conditions using the nonparametric regression

Cited by 19 publications

References 13 publications

Performances Evaluation of GMM-UBM and GMM-SVM for Speaker Recognition in Realistic World

Performances Evaluation of GMM-UBM and GMM-SVM for Speaker Recognition in Realistic World

Unsupervised rapid speaker adaptation based on selective eigenvoice merging for user-specific voice interaction

Improving Speaker Verification Robustness by Front-End Diversity and Score Level Fusion

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