Speaker Verification Using Deep Neural Networks: A Review

Irum, Amna; Ahmad, Sahrim

doi:10.18178/ijmlc.2019.9.1.760

Cited by 23 publications

(14 citation statements)

References 34 publications

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“…Adopting the modified MFCC feature vector has improved the recognition accuracy by 2.43% approximately. A huge research effort has been devoted to developing speaker modeling, including Gaussian mixture model (GMM) [ 18 ], GMM supervector with support vector machine (SVM) [ 19 ], and i-vector system [ 20 , 21 ], deep learning systems [ 6 , 22 , 23 ], normalization [ 24 , 25 ] and channel compensation techniques [ 26 ], and adaptation techniques [ 27 , 28 ] to reduce the effect of these variations on the performance of the speaker recognition system. Impressive progress has been achieved in addressing external-based source of variations, particularly channel variations and environmental and background distortion [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

“…A speaker identification system for social human–robot interaction, which is the main motivation of this research, should be able to extract a voice signature from unconstrained utterances while maintains its performance at challenging scenarios including various low signal-to-noise ratio and short length of utterance as well as different types of noise. However, most of the state-of-the-art speaker recognition systems, including those based on deep learning algorithms, have achieved significant performance on verification tasks which are not suitable for social human–robot interaction [ 4 , 5 , 6 ]. In speaker diarisation, which is a key feature for social robots, also known as “who spoke when”, a speech signal is partitioned into homogenous segments according to speaker identity [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation

Al-Qaderi

Lahamer

Rad

2021

Sensors

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We present a new architecture to address the challenges of speaker identification that arise in interaction of humans with social robots. Though deep learning systems have led to impressive performance in many speech applications, limited speech data at training stage and short utterances with background noise at test stage present challenges and are still open problems as no optimum solution has been reported to date. The proposed design employs a generative model namely the Gaussian mixture model (GMM) and a discriminative model—support vector machine (SVM) classifiers as well as prosodic features and short-term spectral features to concurrently classify a speaker’s gender and his/her identity. The proposed architecture works in a semi-sequential manner consisting of two stages: the first classifier exploits the prosodic features to determine the speaker’s gender which in turn is used with the short-term spectral features as inputs to the second classifier system in order to identify the speaker. The second classifier system employs two types of short-term spectral features; namely mel-frequency cepstral coefficients (MFCC) and gammatone frequency cepstral coefficients (GFCC) as well as gender information as inputs to two different classifiers (GMM and GMM supervector-based SVM) which in total leads to construction of four classifiers. The outputs from the second stage classifiers; namely GMM-MFCC maximum likelihood classifier (MLC), GMM-GFCC MLC, GMM-MFCC supervector SVM, and GMM-GFCC supervector SVM are fused at score level by the weighted Borda count approach. The weight factors are computed on the fly via Mamdani fuzzy inference system that its inputs are the signal to noise ratio and the length of utterance. Experimental evaluations suggest that the proposed architecture and the fusion framework are promising and can improve the recognition performance of the system in challenging environments where the signal-to-noise ratio is low, and the length of utterance is short; such scenarios often arise in social robot interactions with humans.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation

Al-Qaderi

Lahamer

Rad

2021

Sensors

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“…First, this paper focuses on the recently development of deep learning based speaker recognition techniques which achieved the state-of-the-art performance in many situations, while most previous overviews are based on traditional speaker recognition methods [1,4,[17][18][19][20][21]. Although the papers [22,23] summarized the deep learning based speaker recognition methods in certain aspects, our paper summarized different subtasks and topics of speaker recognition from new perspectives. Specifically, [22] presents an overview to the potential threats of adversarial attacks to speaker verification as well as the spoofing countermeasures, which is not the focus of this overview.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, [22] presents an overview to the potential threats of adversarial attacks to speaker verification as well as the spoofing countermeasures, which is not the focus of this overview. We provide a broad and comprehensive overview to a wide aspects of speaker verification, speaker diarization and domain adaptation etc, most of which have not be mentioned in [23].…”

Section: Introductionmentioning

confidence: 99%

Speaker Recognition Based on Deep Learning: An Overview

Bai

Zhang

2020

Preprint

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Speaker recognition is a task of identifying persons from their voices. Recently, deep learning has dramatically revolutionized speaker recognition. However, there is lack of comprehensive reviews on the exciting progress. In this paper, we review several major subtasks of speaker recognition, including speaker verification, identification, diarization, and robust speaker recognition, with a focus on deep-learning-based methods. Because the major advantage of deep learning over conventional methods is its representation ability, which is able to produce highly abstract embedding features from utterances, we first pay close attention to deep-learning-based speaker feature extraction, including the inputs, network structures, temporal pooling strategies, and objective functions respectively, which are the fundamental components of many speaker recognition subtasks. Then, we make an overview of speaker diarization, with an emphasis of recent supervised, end-to-end, and online diarization. Finally, we survey robust speaker recognition from the perspectives of domain adaptation and speech enhancement, which are two major approaches of dealing with domain mismatch and noise problems. Popular and recently released corpora are listed at the end of the paper.

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“…Recently, some techniques for speaker verification have used deep neural networks (DNNs). In [15], a revision containing nine different techniques by employing DNN was presented. The results hit 0.2% and 0.88% for comparisons based on a dependent and independent text, respectively.…”

Section: Introductionmentioning

confidence: 99%

Forensic Speaker Verification Using Ordinary Least Squares

Machado

Filho

Oliveira

2019

Sensors

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In Brazil, the recognition of speakers for forensic purposes still relies on a subjectivity-based decision-making process through a results analysis of untrustworthy techniques. Owing to the lack of a voice database, speaker verification is currently applied to samples specifically collected for confrontation. However, speaker comparative analysis via contested discourse requires the collection of an excessive amount of voice samples for a series of individuals. Further, the recognition system must inform who is the most compatible with the contested voice from pre-selected individuals. Accordingly, this paper proposes using a combination of linear predictive coding (LPC) and ordinary least squares (OLS) as a speaker verification tool for forensic analysis. The proposed recognition technique establishes confidence and similarity upon which to base forensic reports, indicating verification of the speaker of the contested discourse. Therefore, in this paper, an accurate, quick, alternative method to help verify the speaker is contributed. After running seven different tests, this study preliminarily achieved a hit rate of 100% considering a limited dataset (Brazilian Portuguese). Furthermore, the developed method extracts a larger number of formants, which are indispensable for statistical comparisons via OLS. The proposed framework is robust at certain levels of noise, for sentences with the suppression of word changes, and with different quality or even meaningful audio time differences.

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Speaker Verification Using Deep Neural Networks: A Review

Cited by 23 publications

References 34 publications

A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation

A Two-Level Speaker Identification System via Fusion of Heterogeneous Classifiers and Complementary Feature Cooperation

Speaker Recognition Based on Deep Learning: An Overview

Forensic Speaker Verification Using Ordinary Least Squares

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