Enhanced forensic speaker verification using multi-run ICA in the presence of environmental noise and reverberation conditions

Al-Ali, Ahmed Kamil Hasan; Senadji, Bouchra; Naik, Ganesh R.

doi:10.1109/icsipa.2017.8120601

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…Based on their result, the MFCC improves the performance of the fuzzy model compared to fast Fourier transformation features [57]. Al-Ali et al enhanced forensic speaker verification based on the fusion features namely the MFCC and Discrete Wavelet Transform (DWT), where their models were evaluated in a noisy environment [58]. Abdul investigated the MFCC feature for speaker identification.…”

Section: Speaker Recognitionmentioning

confidence: 99%

Mel Frequency Cepstral Coefficient and its Applications: A Review

2022

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Feature extraction and representation has significant impact on the performance of any machine learning method. Mel Frequency Cepstrum Coefficient (MFCC) is designed to model features of audio signal and is widely used in various fields. This paper aims to review the applications that the MFCC is used for in addition to some issues that facing the MFCC computation and its impact on the model performance. These issues include the use of MFCC for non-acoustic signals, adopting the MFCC alone or combining it with other features, the use of time series versus global representation of the MFCC, following the standard form of the MFCC computation versus modifying its parameters, and supplying the traditional machine learning methods versus the deep learning methods..

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Section: Speaker Recognitionmentioning

confidence: 99%

Mel Frequency Cepstral Coefficient and its Applications: A Review

2022

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“…Another method to improve further the performance of a speaker verification system is using fusion [10,108]. There are two frequently used fusion techniques for speaker verification, namely, score fusion [55] and feature fusion [5]. Score fusion is a method used to make a final decision by matching the scores output from more than one biometric modal.…”

Section: Searching Speaker Modelmentioning

confidence: 99%

A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction

Tan

Hijazi

Khamis

et al. 2021

Multimed Tools Appl

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The emergence of biometric technology provides enhanced security compared to the traditional identification and authentication techniques that were less efficient and secure. Despite the advantages brought by biometric technology, the existing biometric systems such as Automatic Speaker Verification (ASV) systems are weak against presentation attacks. A presentation attack is a spoofing attack launched to subvert an ASV system to gain access to the system. Though numerous Presentation Attack Detection (PAD) systems were reported in the literature, a systematic survey that describes the current state of research and application is unavailable. This paper presents a systematic analysis of the state-of-the-art voice PAD systems to promote further advancement in this area. The objectives of this paper are two folds: (i) to understand the nature of recent work on PAD systems, and (ii) to identify areas that require additional research. From the survey, a taxonomy of voice PAD and the trend analysis of recent work on PAD systems were built and presented, whereby the recent and relevant articles including articles from Interspeech and ICASSP Conferences, mostly indexed by Scopus, published between 2015 and 2021 were considered. A total of 172 articles were surveyed in this work. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established. The findings of this survey present the limitation of recent works, which include spoof-type dependent PAD. Consequently, the future direction of work on voice PAD for interested researchers is established.

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“…Decoding such information has been of benefit to a number of different speech processing tasks such as speech and speaker recognition, as well as Speech Emotion Recognition (SER). After long-term research, both speech and speaker recognition have been addressed pretty well [1][2][3][4][5], while SER remain difficult, especially in the presence of noise.…”

Section: Introductionmentioning

confidence: 99%

An Investigation of a Feature-Level Fusion for Noisy Speech Emotion Recognition

2019

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Because one of the key issues in improving the performance of Speech Emotion Recognition (SER) systems is the choice of an effective feature representation, most of the research has focused on developing a feature level fusion using a large set of features. In our study, we propose a relatively low-dimensional feature set that combines three features: baseline Mel Frequency Cepstral Coefficients (MFCCs), MFCCs derived from Discrete Wavelet Transform (DWT) sub-band coefficients that are denoted as DMFCC, and pitch based features. Moreover, the performance of the proposed feature extraction method is evaluated in clean conditions and in the presence of several real-world noises. Furthermore, conventional Machine Learning (ML) and Deep Learning (DL) classifiers are employed for comparison. The proposal is tested using speech utterances of both of the Berlin German Emotional Database (EMO-DB) and Interactive Emotional Dyadic Motion Capture (IEMOCAP) speech databases through speaker independent experiments. Experimental results show improvement in speech emotion detection over baselines.

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Enhanced forensic speaker verification using multi-run ICA in the presence of environmental noise and reverberation conditions

Cited by 9 publications

References 21 publications

Mel Frequency Cepstral Coefficient and its Applications: A Review

Mel Frequency Cepstral Coefficient and its Applications: A Review

A survey on presentation attack detection for automatic speaker verification systems: State-of-the-art, taxonomy, issues and future direction

An Investigation of a Feature-Level Fusion for Noisy Speech Emotion Recognition

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