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Telephony text-prompted speaker verification using i-vector representation

, Article ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 19 April 2014 through 24 April 2014 ; Volume 2015-August , 2015 , Pages 4839-4843 ; 15206149 (ISSN) ; 9781467369978 (ISBN) Zeinali, H ; Kalantari, E ; Sameti, H ; Hadian, H ; Sharif University of Technology

Institute of Electrical and Electronics Engineers Inc 2015

Abstract

I-vectors have proved to be the most effective features for text-independent speaker verification in recent researches. In this article a new scheme is proposed to utilize i-vectors in text-prompted speaker verification in a simple while effective manner. In order to examine this scheme empirically, a telephony dataset of Persian month names is introduced. Experiments show that the proposed scheme reduces the EER by 31% compared to the state-of-the-art State-GMM-MAP method. Furthermore it is shown that using HMM instead of GMM for universal background modeling leads to 15% reduction in EER

Speaker Verification using Limited Enrollment Data

, M.Sc. Thesis Sharif University of Technology Kalantari, Elaheh (Author) ; Sameti, Hossein (Supervisor)

Abstract

In this thesis, we investigate speaker verification as a biometric technology to verify a person based on his/her claim. Text-dependent speaker verification systems are preferred in commercial and security applications and these systems have better performance in limited data condition based on a prior knowledge about speakers that are assumed to be cooperative. Limited amount of enrollment data is a major concern in this thesis. Speaker dependent model construction and channel variability issues on telephone-based text-dependent speaker verification applications are surveyed. Due to the lack of an appropriate database for the task, we collected a database which is referred to as text-prompt...

محتواي کتاب

Speaker recognition with random digit strings using uncertainty normalized HMM-Based i-Vectors

, Article IEEE/ACM Transactions on Audio Speech and Language Processing ; Volume 27, Issue 11 , 2019 , Pages 1815-1825 ; 23299290 (ISSN) Maghsoodi, N ; Sameti, H ; Zeinali, H ; Stafylakis, T ; Sharif University of Technology

Institute of Electrical and Electronics Engineers Inc 2019

Abstract

In this paper, we combine Hidden Markov Models HMMs with i-vector extractors to address the problem of text-dependent speaker recognition with random digit strings. We employ digit-specific HMMs to segment the utterances into digits, to perform frame alignment to HMM states and to extract Baum-Welch statistics. By making use of the natural partition of input features into digits, we train digit-specific i-vector extractors on top of each HMM and we extract well-localized i-vectors, each modelling merely the phonetic content corresponding to a single digit. We then examine ways to perform channel and uncertainty compensation, and we propose a novel method for using the uncertainty in the...