Constrained temporal structure for text-dependent speaker verification

Larcher, Anthony; Bonastre, Jean-François; Mason, John S.

doi:10.1016/j.dsp.2013.07.007

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…Note that the lower the equal error rate (EER) value, the higher the accuracy of the system. Ergonomic constraints and limited amount of computing resources were among the motivations of Larcher et al while presenting their study about speaker recognition engines working on mobile devices [52]. Such systems may show efficient performance in classical context; however, their limitations will appear when restricting the quantity of speech data.…”

Section: Published Work In the Year 2013mentioning

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 2013mentioning

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

View full text Add to dashboard Cite

show abstract

“…In [8], the LFCC feature extraction technique with an MYLDEA Dataset and three modeling techniques are proposed. LFCC is a feature extraction technique used in the field of speaker recognition.…”

Section: Linear Frequency Cepstral Coefficients (Lfcc)mentioning

confidence: 99%

Speaker Recognition Systems in the Last Decade – A Survey

Ahmed¹,

Hassan²

2021

ETJ

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Speaker Recognition Defined by the process of recognizing a person by his\her voice through specific features that extract from his\her voice signal. An Automatic Speaker recognition (ASP) is a biometric authentication system. In the last decade, many advances in the speaker recognition field have been attained, along with many techniques in feature extraction and modeling phases. In this paper, we present an overview of the most recent works in ASP technology. The study makes an effort to discuss several modeling ASP techniques like Gaussian Mixture Model GMM, Vector Quantization (VQ), and Clustering Algorithms. Also, several feature extraction techniques like Linear Predictive Coding (LPC) and Mel frequency cepstral coefficients (MFCC) are examined. Finally, as a result of this study, we found MFCC and GMM methods could be considered as the most successful techniques in the field of speaker recognition so far.

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“…In context of mobile devices, ASV engines are susceptible to suffer from limited computing resources and ergonomic constraints. A GMM‐UBM extension was prescribed in [78] to compensate the situations characterised by constrained amount of enrolment data and computation facility, typically available on hand‐held mobile devices. The key contribution was influenced from the idea of incorporation of temporal structure information of speech using pass‐phrases customised by the client and new Markov model structures in addition to it.…”

Section: Research In Asv On Short Utterancesmentioning

confidence: 99%

Speaker verification with short utterances: a review of challenges, trends and opportunities

2017

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Automatic speaker verification (ASV) technology now reports a reasonable level of accuracy in its applications in voice-based biometric systems. However, it requires adequate amount of speech data for enrolment and verification; otherwise, the performance becomes considerably degraded. For this reason, the trade-off between the convenience and security is difficult to maintain in practical scenarios. The utterance duration remains a critical issue while deploying a voice biometric system in real-world applications. A large amount of research work has been carried out to address the limited data issue within the scope of SV. The advancements and research activities in mitigating the challenges due to short utterance have seen a significant rise in recent times. In this study, the authors present an extensive survey of SV with short utterances considering the studies from recent past and include latest research offering various solutions and analyses. The review also summarises the major findings of the studies of duration variability problem in ASV systems. Finally, they discuss a number of possible future directions promoting further research in this field. 2 Brief overview of ASV An ASV system includes three fundamental modules [1, 2]: a feature extraction unit, which transforms the speech signal in a compact form, a statistical modelling unit to characterise the extracted features, and finally a classification module to classify a test speech. 2.1 Feature extraction approaches The state-of-the-art ASV systems use three major types of feature extraction techniques: sub-segmental, segmental and suprasegmental analyses. Speech signals analysed using the frame size

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Constrained temporal structure for text-dependent speaker verification

Cited by 10 publications

References 24 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

Speaker Recognition Systems in the Last Decade – A Survey

Speaker verification with short utterances: a review of challenges, trends and opportunities

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