Blind Speech Signal Quality Estimation for Speaker Verification Systems

2020 ◽  
Author(s):  
Galina Lavrentyeva ◽  
Marina Volkova ◽  
Anastasia Avdeeva ◽  
Sergey Novoselov ◽  
Artem Gorlanov ◽  
...  
Keyword(s):  
Speech Signal ◽  
Speaker Verification ◽  
Signal Quality ◽  
Quality Estimation ◽  
Verification Systems

Robustness Speaker Recognition Based on Feature Space in Clean and Noisy Condition

2019 ◽  
Vol 9 (4) ◽  
pp. 497-506 ◽  
Author(s):  
Khamis A. Al-Karawi
Keyword(s):  
Speech Processing ◽  
Speaker Recognition ◽  
System Performance ◽  
Speaker Verification ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Feature Space ◽  
Signal To Noise ◽  
Verification Systems ◽  
Noisy Condition

Background & Objective: Speaker Recognition (SR) techniques have been developed into a relatively mature status over the past few decades through development work. Existing methods typically use robust features extracted from clean speech signals, and therefore in idealized conditions can achieve very high recognition accuracy. For critical applications, such as security and forensics, robustness and reliability of the system are crucial. Methods: The background noise and reverberation as often occur in many real-world applications are known to compromise recognition performance. To improve the performance of speaker verification systems, an effective and robust technique is proposed to extract features for speech processing, capable of operating in the clean and noisy condition. Mel Frequency Cepstrum Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GFCC) are the mature techniques and the most common features, which are used for speaker recognition. MFCCs are calculated from the log energies in frequency bands distributed over a mel scale. While GFCC has been acquired from a bank of Gammatone filters, which was originally suggested to model human cochlear filtering. This paper investigates the performance of GFCC and the conventional MFCC feature in clean and noisy conditions. The effects of the Signal-to-Noise Ratio (SNR) and language mismatch on the system performance have been taken into account in this work. Conclusion: Experimental results have shown significant improvement in system performance in terms of reduced equal error rate and detection error trade-off. Performance in terms of recognition rates under various types of noise, various Signal-to-Noise Ratios (SNRs) was quantified via simulation. Results of the study are also presented and discussed.

U-NORM Likelihood Normalization in PIN-Based Speaker Verification Systems

2003 ◽  
pp. 208-213 ◽  
Author(s):  
D. Garcia-Romero ◽  
J. Gonzalez-Rodriguez ◽  
J. Fierrez-Aguilar ◽  
J. Ortega-Garcia
Keyword(s):  
Speaker Verification ◽  
Verification Systems

scholarly journals Enhancement of the perceptive quality of the noisy speech signal by using of DFF-FBC algorithm

2009 ◽  
Vol 22 (3) ◽  
pp. 391-404
Author(s):  
Zoran Milivojevic ◽  
Dragisa Balaneskovic
Keyword(s):  
Speech Signal ◽  
Mean Opinion Score ◽  
Signal Quality ◽  
Test Results ◽  
Frequency Filtering ◽  
Noisy Speech ◽  
Voice Signal ◽  
Opinion Score

This paper presents an algorithm for enhancement of the noisy speech signal quality. This algorithm is based on the dissonant frequency filtering (DFF), F#, B and C# in relation to the frequency of the primary tone C (DFF-FBC algorithm). By means of the subjective Mean Opinion Score (MOS) test, the effect of the enhancement of the speech signal quality was analyzed. The analysis of the MOS test results, presented in the second part of this paper, points out to the enhancement of the noisy speech signal quality in the presence of superimposed noises. Especially good results have been found with Husky Voice signal. .

scholarly journals Speaker verification system based on articulatory information from ultrasound recordings

DYNA ◽  
2020 ◽  
Vol 87 (213) ◽  
pp. 9-16
Author(s):  
Franklin Alexander Sepulveda Sepulveda ◽  
Dagoberto Porras-Plata ◽  
Milton Sarria-Paja
Keyword(s):  
State Of The Art ◽  
Speaker Verification ◽  
Environmental Noise ◽  
Speech Signals ◽  
Acoustic Information ◽  
Current State ◽  
Verification System ◽  
Vocal Effort ◽  
Ultrasound System ◽  
Verification Systems

Current state-of-the-art speaker verification (SV) systems are known to be strongly affected by unexpected variability presented during testing, such as environmental noise or changes in vocal effort. In this work, we analyze and evaluate articulatory information of the tongue's movement as a means to improve the performance of speaker verification systems. We use a Spanish database, where besides the speech signals, we also include articulatory information that was acquired with an ultrasound system. Two groups of features are proposed to represent the articulatory information, and the obtained performance is compared to an SV system trained only with acoustic information. Our results show that the proposed features contain highly discriminative information, and they are related to speaker identity; furthermore, these features can be used to complement and improve existing systems by combining such information with cepstral coefficients at the feature level.

Sign in / Sign up

Export Citation Format

Share Document