Environment adaptation for robust speaker verification by cascading maximum likelihood linear regression and reinforced learning

Yiu, Kwok-Kwong; Mak, Man‐Wai; Kung, Sun‐Yuan

doi:10.1016/j.csl.2006.05.001

Cited by 29 publications

(4 citation statements)

References 15 publications

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“…It has been shown that both mean normalization and bandpass filtering can minimize the filtering effect of linear channels [11,12]. However, these techniques may cause performance degradation when both training and recognition are derived from the same acoustic environment [4].…”

Section: Blind Compensationmentioning

confidence: 99%

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Probabilistic feature-based transformation for speaker verification over telephone networks

2007

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Feature transformation aims to reduce the effects of channel-and handset-distortion in telephone-based speaker verification. This paper compares several feature transformation techniques and evaluates their verification performance and computation time under the 2000 NIST speaker recognition evaluation protocol. Techniques compared include feature mapping (FM), stochastic feature transformation (SFT), blind stochastic feature transformation (BSFT), feature warping (FW), and short-time Gaussianization (STG). The paper proposes a probabilistic feature mapping (PFM) in which the mapped features depend not only on the top-1 decoded Gaussian but also on the posterior probabilities of other Gaussians in the root model. The paper also proposes speeding up the computation of PFM and BSFT parameters by considering the top few Gaussians only. Results show that PFM performs slightly better than FM and that the fast approach can reduce computation time substantially. Among the approaches investigated, the fast BSFT (fBSFT) strikes a good balance between computational complexity and error rates, and FW and STG are the best in terms of error rates but with higher computational complexity. It was also found that fusion of the scores derived from systems using fBSFT and STG can reduce the error rate further. This study advocates that fBSFT, FW, and STG have the highest potential for robust speaker verification over telephone networks because they achieve good performance without any a priori knowledge of the communication channel.

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Section: Blind Compensationmentioning

confidence: 99%

“…Channel compensation can be applied in feature space [1,2], model space [3,4] or score space [5]. One advantage of feature-space compensation is that it is not necessary to modify the speaker models after training.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic feature-based transformation for speaker verification over telephone networks

2007

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“…1024-mixture GMM. The speaker verification protocol for HTIMIT described in [28] [29] was taken as a guideline. More precisely, 100 speakers were randomly chosen out of the total 384 to form the client set.…”

Section: ) Database Descriptionmentioning

confidence: 99%

A Fast Parts-Based Approach to Speaker Verification Using Boosted Slice Classifiers

Magimai-Doss

Marcel

2012

IEEE Trans.Inform.Forensic Secur.

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Abstract-Speaker verification on portable devices like smartphones is gradually becoming popular. In this context, two issues need to be considered: 1) such devices have relatively limited computation resources, and 2) they are liable to be used everywhere, possibly in very noisy, uncontrolled environments. This work aims to address both these issues by proposing a computationally efficient yet robust speaker verification system. This novel parts-based system draws inspiration from face and object detection systems in the computer vision domain. The system involves boosted ensembles of simple threshold-based classifiers. It uses a novel set of features extracted from speech spectra, called "slice features". The performance of the proposed system was evaluated through extensive studies involving a wide range of experimental conditions using the TIMIT, HTIMIT and MOBIO corpus, against standard cepstral features and Gaussian Mixture Model-based speaker verification systems.

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“…The goal of channel compensation is to achieve performance approaching that of a "matched condition" system. Channel compensation can be applied in feature space [1,2], model space [3,4] or score space [5]. One advantage of featurespace compensation is that it is not necessary to modify the speaker models after training.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Feature Transformation for Channel Robust Speaker Verification

Mak

Yiu

2006

2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing

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Feature transformation plays an important role in robust speaker verification over telephone networks. This paper compares several feature transformation techniques and evaluates their verification performance and computation time under the 2000 NIST speaker recognition evaluation protocol. Techniques compared include feature mapping (FM), stochastic feature transformation (SFT), and blind stochastic feature transformation (BSFT). The paper proposes a probabilistic feature mapping (PFM) in which the mapped features depend not only on the top-1 decoded Gaussian but also on the posterior probabilities of other Gaussians in the root model. The paper also proposes speeding up the computation of PFM and BSFT parameters by considering the top few Gaussians only. Results show that PFM performs slightly better than FM and that the fast approach can reduce computation time substantially. Among the approaches investigated, the fast BSFT is found to have the highest potential for robust speaker verification over telephone networks because it can achieve good performance without any a priori knowledge of the communication channel. It was also found that fusion of the scores derived from systems using BSFT and PFM can reduce the error rate further.

show abstract

Environment adaptation for robust speaker verification by cascading maximum likelihood linear regression and reinforced learning

Cited by 29 publications

References 15 publications

Probabilistic feature-based transformation for speaker verification over telephone networks

Probabilistic feature-based transformation for speaker verification over telephone networks

A Fast Parts-Based Approach to Speaker Verification Using Boosted Slice Classifiers

Probabilistic Feature Transformation for Channel Robust Speaker Verification

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