An investigation on speaker vector-based speaker identification under noisy conditions

Goto, Y.; Akatsu, T.; Katoh, Masaharu; Kosaka, Tetsuo; Kohda, Masaki

doi:10.1109/icalip.2008.4590119

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…False acceptance error consists of accepting identity claim from an imposter (Goto et al, 2008). False rejection error happens when a valid identity claim is rejected.…”

Section: Feature Matchingmentioning

confidence: 99%

“…The codebook is a set of cells in a multidimensional space. Each cell defines a small part of the total space and contains a point centered within the cell, the centroid (Goto et al, 2008). The cepstral coefficients derived from each frame are regarded as a vector (a point) in the space and thereby belonging to one of the cells.…”

Section: Speaker Modeling Techniquementioning

confidence: 99%

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Feature Extraction Techniques

Computer-Based Design and Manufacturing

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Problem statement:This study introduces a new method for speaker verification system by fusing two different feature extraction methods to improve the recognition accuracy and security. Approach: The proposed system uses Mel frequency cepstral coefficients for speaker identification and Modified MFCC for verification. For speaker modeling vector quantization is used. Results: The proposed system was investigated the effect of the different length segmental feature as well as speaker modeling for speaker recognition. The performance was evaluated against 1000 speakers for 10 different languages with duration of 10 sec for training the system and for testing 5 sec. duration samples were used. Conclusion/Recommendations: Experimental results of the proposed system showed that higher recognition accuracy of 93% is achieved by increasing the number of filter banks used for feature extraction method, more competitive with existing system using vector quantization with lesser computational complexity. The system efficiency may further be improved using other speaker modeling techniques like GMM, HMM.

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“…False acceptance error consists of accepting identity claim from an imposter (Goto et al, 2008). False rejection error happens when a valid identity claim is rejected.…”

Section: Feature Matchingmentioning

confidence: 99%

Section: Speaker Modeling Techniquementioning

confidence: 99%

Feature Extraction Techniques

Computer-Based Design and Manufacturing

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Improvement of Speaker Vector-Based Speaker Verification

Naoki

Kosaka

Kato

et al. 2009

2009 Fifth International Conference on Information Assurance and Security

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Comparison of I-vector and GMM-UBM approaches to speaker identification with TIMIT and NIST 2008 databases in challenging environments

Al-Kaltakchi

Woo

Dlay

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, two models, the I-vector and the Gaussian Mixture Model-Universal Background Model (GMM-UBM), are compared for the speaker identification task. Four feature combinations of I-vectors with seven fusion techniques are considered: maximum, mean, weighted sum, cumulative, interleaving and concatenated for both two and four features. In addition, an Extreme Learning Machine (ELM) is exploited to identify speakers, and then Speaker Identification Accuracy (SIA) is calculated. Both systems are evaluated for 120 speakers from the TIMIT and NIST 2008 databases for clean speech. Furthermore, a comprehensive evaluation is made under Additive White Gaussian Noise (AWGN) conditions and with three types of Non Stationary Noise (NSN), both with and without handset effects for the TIMIT database. The results show that the Ivector approach is better than the GMM-UBM for both clean and AWGN conditions without a handset. However, the GMM-UBM had better accuracy for NSN types.

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An investigation on speaker vector-based speaker identification under noisy conditions

Cited by 5 publications

References 6 publications

Feature Extraction Techniques

Feature Extraction Techniques

Improvement of Speaker Vector-Based Speaker Verification

Comparison of I-vector and GMM-UBM approaches to speaker identification with TIMIT and NIST 2008 databases in challenging environments

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