Adaptive wavelet shrinkage for noise robust speaker recognition

Govindan, Sumithra Manimegalai; Duraisamy, Prakash; Yuan, Xiaohui

doi:10.1016/j.dsp.2014.06.007

Cited by 33 publications

(11 citation statements)

References 27 publications

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“…The noise affecting the voice signal varies greatly and a priori noise model is usually unavailable. To deal with this issue, Govindan et al proposed a speaker recognition procedure that employs an adaptive wavelet shrinkage method for noise suppression, where wavelet sub-band coefficient thresholds, which are proportional to the noise contamination, are automatically computed [33].…”

Section: Published Work In the Year 2014mentioning

confidence: 99%

Analysis of Methods and Techniques Used for Speaker Identification, Recognition, and Verification: A Study on Quarter-Century Research Outcomes

Mohammed

Aljebory

Rasheed

et al. 2021

eijs

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The theories and applications of speaker identification, recognition, and verification are among the well-established fields. Many publications and advances in the relevant products are still emerging. In this paper, research-related publications of the past 25 years (from 1996 to 2020) were studied and analysed. Our main focus was on speaker identification, speaker recognition, and speaker verification. The study was carried out using the Science Direct databases. Several references, such as review articles, research articles, encyclopaedia, book chapters, conference abstracts, and others, were categorized and investigated. Summary of these kinds of literature is presented in this paper, together with statistical analyses to represent the publications and their categories over the mentioned period. Important information, including the dataset used, the size of the data adopted, the implemented methods, and the accuracy of the obtained results in the analysed research, are extracted from the explored publications and tabulated. The results show that the sum of published research articles is outnumbering other categories of publications. The number of researches in speech and speaker identification, recognition, and verification shows an increasing trend. Based on the normalized comparative factors of research publications, we found that many of them reached a high level of accuracy in their findings; hence the significantly superior techniques were derived and discussed for future researches. This survey paper would be beneficial for all those who wish to enhance their researches in the area of voice identification, recognition, and verification.

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Section: Published Work In the Year 2014mentioning

confidence: 99%

Analysis of Methods and Techniques Used for Speaker Identification, Recognition, and Verification: A Study on Quarter-Century Research Outcomes

Mohammed

Aljebory

Rasheed

et al. 2021

eijs

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show abstract

“…The experiments proved that joint probability models (where clean, and noisy vectors are concatenated to train a single GMM) gives higher EER reduction values. Another method, which is also independent from a prior knowledge of noise, is proposed in [53], based on bionic wavelet transform [93]. In this method, coefficients with a small amplitude are considered as noise features.…”

Section: Robust Features Against Additive Noisementioning

confidence: 99%

A Review on Feature Extraction for Speaker Recognition under Degraded Conditions

DİŞKEN

Tüfekçi

Sarıbulut

et al. 2016

IETE Technical Review

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Speech is a signal that includes speaker's emotion, characteristic specification, phonemeinformation etc. Various methods have been proposed for speaker recognition by extracting specifications of a given utterance. Among them, short-term cepstral features are used excessively in speech, and speaker recognition areas because of their low complexity, and high performance in controlled environments. On the other hand, their performances decrease dramatically under degraded conditions such as channel mismatch, additive noise, emotional variability, etc. In this paper, a literature review on speaker-specific information extraction from speech is presented by considering the latest studies offering solutions to the aforementioned problem. The studies are categorized in three groups considering their robustness against channel mismatch, additive noise, and other degradations such as vocal effort, emotion mismatch, etc. For a more understandable representation, they are also classified into two tables by utilizing their classification methods, and used data-sets.

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“…In speaker identification (SID) systems, the extracted features from each speech frame are a crucial factor for building a reliable identification system. In clean environments, the identification system performs well, but in noisy environments, the distribution models of the features that extracted from the noisy speech will not matches the clean features distribution model that built in training phase [4]. To overcome this problem, the researchers applied many approaches to achieve this goal.…”

Section: Introductionmentioning

confidence: 99%

“…S.M. Govidan et al [4] used Adaptive bionic wavelet shrinkage (ABWS) which is a speech enhancement technique that's used to suppress the additive noise and increase the accuracy of the speaker recognition system, a double threshold is computed and applied based on estimated noise on each sub-band decomposed by adaptive bionic wavelet coefficients, a good results was reported in variety of noise types and levels. Y. Xu et al [11] obtained clean speech signal from noisy one with deep neural network (DNN) by calculating log-power spectra of noisy speech signal then mapping noisy to clean data using a well-trained DNN, the mapping function was trained with DNN over 104 noise types with 2500 hour of training.…”

Section: Introductionmentioning

confidence: 99%

Adaptive wavelet thresholding with robust hybrid features for text-independent speaker identification system

Alabbasi

Jalil

Hasan

2020

IJECE

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The robustness of speaker identification system over additive noise channel is crucial for real-world applications. In speaker identification (SID) systems, the extracted features from each speech frame are an essential factor for building a reliable identification system. For clean environments, the identification system works well; in noisy environments, there is an additive noise, which is affect the system. To eliminate the problem of additive noise and to achieve a high accuracy in speaker identification system a proposed algorithm for feature extraction based on speech enhancement and a combined features is presents. In this paper, a wavelet thresholding pre-processing stage, and feature warping (FW) techniques are used with two combined features named power normalized cepstral coefficients (PNCC) and gammatone frequency cepstral coefficients (GFCC) to improve the identification system robustness against different types of additive noises. Universal Background Model Gaussian Mixture Model (UBM-GMM) is used for features matching between the claim and actual speakers. The results showed performance improvement for the proposed feature extraction algorithm of identification system comparing with conventional features over most types of noises and different SNR ratios.

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Adaptive wavelet shrinkage for noise robust speaker recognition

Cited by 33 publications

References 27 publications

Analysis of Methods and Techniques Used for Speaker Identification, Recognition, and Verification: A Study on Quarter-Century Research Outcomes

Analysis of Methods and Techniques Used for Speaker Identification, Recognition, and Verification: A Study on Quarter-Century Research Outcomes

A Review on Feature Extraction for Speaker Recognition under Degraded Conditions

Adaptive wavelet thresholding with robust hybrid features for text-independent speaker identification system

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