612 research outputs found
Typicality extraction in a Speaker Binary Keys model
International audienceIn the field of speaker recognition, the recently proposed notion of "Speaker Binary Key" provides a representation of each acoustic frame in a discriminant binary space. This approach relies on an unique acoustic model composed by a large set of speaker specific local likelihood peaks (called specificities). The model proposes a spatial coverage where each frame is characterized in terms of neighborhood. The most frequent specificities, picked up to represent the whole utterance, generate a binary key vector. The flexibility of this modeling allows to capture non-parametric behaviors. In this paper, we introduce a concept of "typicality" between binary keys, with a discriminant goal. We describe an algorithm able to extract such typicalities, which involves a singular value decomposition in a binary space. The theoretical aspects of this decomposition as well as its potential in terms of future developments are presented. All the propositions are also experimentally validated using NIST SRE 2008 framework
Anti-spoofing Methods for Automatic SpeakerVerification System
Growing interest in automatic speaker verification (ASV)systems has lead to
significant quality improvement of spoofing attackson them. Many research works
confirm that despite the low equal er-ror rate (EER) ASV systems are still
vulnerable to spoofing attacks. Inthis work we overview different acoustic
feature spaces and classifiersto determine reliable and robust countermeasures
against spoofing at-tacks. We compared several spoofing detection systems,
presented so far,on the development and evaluation datasets of the Automatic
SpeakerVerification Spoofing and Countermeasures (ASVspoof) Challenge
2015.Experimental results presented in this paper demonstrate that the useof
magnitude and phase information combination provides a substantialinput into
the efficiency of the spoofing detection systems. Also wavelet-based features
show impressive results in terms of equal error rate. Inour overview we compare
spoofing performance for systems based on dif-ferent classifiers. Comparison
results demonstrate that the linear SVMclassifier outperforms the conventional
GMM approach. However, manyresearchers inspired by the great success of deep
neural networks (DNN)approaches in the automatic speech recognition, applied
DNN in thespoofing detection task and obtained quite low EER for known and
un-known type of spoofing attacks.Comment: 12 pages, 0 figures, published in Springer Communications in Computer
and Information Science (CCIS) vol. 66
Bio : A Mulrimodal biometric authentication system for person identification and verification
Not availabl
Text-independent speaker recognition
This research presents new text-independent speaker recognition system with multivariate tools such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) embedded into the recognition system after the feature extraction step. The proposed approach evaluates the performance of such a recognition system when trained and used in clean and noisy environments. Additive white Gaussian noise and convolutive noise are added. Experiments were carried out to investigate the robust ability of PCA and ICA using the designed approach. The application of ICA improved the performance of the speaker recognition model when compared to PCA. Experimental results show that use of ICA enabled extraction of higher order statistics thereby capturing speaker dependent statistical cues in a text-independent recognition system. The results show that ICA has a better de-correlation and dimension reduction property than PCA. To simulate a multi environment system, we trained our model such that every time a new speech signal was read, it was contaminated with different types of noises and stored in the database. Results also show that ICA outperforms PCA under adverse environments. This is verified by computing recognition accuracy rates obtained when the designed system was tested for different train and test SNR conditions with additive white Gaussian noise and test delay conditions with echo effect
Automatic speech feature extraction using a convolutional restricted boltzmann machine
A dissertation submitted to the Faculty of Science, University of
the Witwatersrand, in fulfillment of the requirements for the degree
of Master of Science
2017Restricted Boltzmann Machines (RBMs) are a statistical learning concept that can
be interpreted as Arti cial Neural Networks. They are capable of learning, in an
unsupervised fashion, a set of features with which to describe a data set. Connected
in series RBMs form a model called a Deep Belief Network (DBN), learning abstract
feature combinations from lower layers. Convolutional RBMs (CRBMs) are a variation
on the RBM architecture in which the learned features are kernels that are convolved
across spatial portions of the input data to generate feature maps identifying if a feature
is detected in a portion of the input data. Features extracted from speech audio data
by a trained CRBM have recently been shown to compete with the state of the art
for a number of speaker identi cation tasks. This project implements a similar CRBM
architecture in order to verify previous work, as well as gain insight into Digital Signal
Processing (DSP), Generative Graphical Models, unsupervised pre-training of Arti cial
Neural Networks, and Machine Learning classi cation tasks. The CRBM architecture
is trained on the TIMIT speech corpus and the learned features veri ed by using them
to train a linear classi er on tasks such as speaker genetic sex classi cation and speaker
identi cation. The implementation is quantitatively proven to successfully learn and
extract a useful feature representation for the given classi cation tasksMT 201
Advanced Biometrics with Deep Learning
Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others
Biometrics
Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about
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