A novel scores fusion approach applied on speaker verification under noisy environments

Asbai, Nassim; Amrouche, Abderrahmane

doi:10.1007/s10772-017-9419-z

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…GMM for scoring fusion. In order to improve the robustness of the speaker verificati under noisy environments, Asbai and Amrouche [26] proposed a new method weighted score fusion. These studies fully prove that the fusion method can effective improve the performance of the speaker recognition system.…”

Section: Proposed Speaker Recognition Systemmentioning

confidence: 99%

“…For the sake of find a solution to the problem, work in [25] utilized the Convolutional Neural Network (CNN) to process the spectrogram of speech signal and combined it with the GMM for scoring fusion. In order to improve the robustness of the speaker verification under noisy environments, Asbai and Amrouche [26] proposed a new method of weighted score fusion. These studies fully prove that the fusion method can effectively improve the performance of the speaker recognition system.…”

Section: Introductionmentioning

confidence: 99%

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Speaker Recognition Based on Fusion of a Deep and Shallow Recombination Gaussian Supervector

Sun

Zou

et al. 2020

Electronics

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Extracting speaker’s personalized feature parameters is vital for speaker recognition. Only one kind of feature cannot fully reflect the speaker’s personality information. In order to represent the speaker’s identity more comprehensively and improve speaker recognition rate, we propose a speaker recognition method based on the fusion feature of a deep and shallow recombination Gaussian supervector. In this method, the deep bottleneck features are first extracted by Deep Neural Network (DNN), which are used for the input of the Gaussian Mixture Model (GMM) to obtain the deep Gaussian supervector. On the other hand, we input the Mel-Frequency Cepstral Coefficient (MFCC) to GMM directly to extract the traditional Gaussian supervector. Finally, the two categories of features are combined in the form of horizontal dimension augmentation. In addition, when the number of speakers to be recognized increases, in order to prevent the system recognition rate from falling sharply, we introduce the optimization algorithm to find the optimal weight before the feature fusion. The experiment results indicate that the speaker recognition rate based on the feature which is fused directly can reach 98.75%, which is 5% and 0.62% higher than the traditional feature and deep bottleneck feature, respectively. When the number of speakers increases, the fusion feature based on optimized weight coefficients can improve the recognition rate by 0.81%. It is validated that our proposed fusion method can effectively consider the complementarity of the different types of features and improve the speaker recognition rate.

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Section: Proposed Speaker Recognition Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Speaker Recognition Based on Fusion of a Deep and Shallow Recombination Gaussian Supervector

Sun

Zou

et al. 2020

Electronics

View full text Add to dashboard Cite

show abstract

“…However, the technique using Gaussian mixture models (GMM) [19] remains the method of choice when dealing with speech emotion recognition [20,21]. Much better scores are achieved by a fusion of different recognition methods, e.g., GMM and SVM in speaker age and gender identification [22] or in speaker verification [23], or SVM and K-nearest neighbour in speech emotion recognition [24]. Another improvement may be achieved by multimodal approach to emotion recognition using a fusion of features extracted from audio signals, text transcriptions, and visual signals of face expressions [25].…”

Section: Introductionmentioning

confidence: 99%

Stress Level Detection and Evaluation from Phonation and PPG Signals Recorded in an Open-Air MRI Device

2021

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This paper deals with two modalities for stress detection and evaluation—vowel phonation speech signal and photo-plethysmography (PPG) signal. The main measurement is carried out in four phases representing different stress conditions for the tested person. The first and last phases are realized in laboratory conditions. The PPG and phonation signals are recorded inside the magnetic resonance imaging scanner working with a weak magnetic field up to 0.2 T in a silent state and/or with a running scan sequence during the middle two phases. From the recorded phonation signal, different speech features are determined for statistical analysis and evaluation by the Gaussian mixture models (GMM) classifier. A database of affective sounds and two databases of emotional speech were used for GMM creation and training. The second part of the developed method gives comparison of results obtained from the statistical description of the sensed PPG wave together with the determined heart rate and Oliva–Roztocil index values. The fusion of results obtained from both modalities gives the final stress level. The performed experiments confirm our working assumption that a fusion of both types of analysis is usable for this task—the final stress level values give better results than the speech or PPG signals alone.

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A new architecture based VAD for speaker diarization/detection systems

Kenai

Ouamour

Guerti

et al. 2019

Int J Speech Technol

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A novel scores fusion approach applied on speaker verification under noisy environments

Cited by 7 publications

References 25 publications

Speaker Recognition Based on Fusion of a Deep and Shallow Recombination Gaussian Supervector

Speaker Recognition Based on Fusion of a Deep and Shallow Recombination Gaussian Supervector

Stress Level Detection and Evaluation from Phonation and PPG Signals Recorded in an Open-Air MRI Device

A new architecture based VAD for speaker diarization/detection systems

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