1,371 research outputs found
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
A Novel Windowing Technique for Efficient Computation of MFCC for Speaker Recognition
In this paper, we propose a novel family of windowing technique to compute
Mel Frequency Cepstral Coefficient (MFCC) for automatic speaker recognition
from speech. The proposed method is based on fundamental property of discrete
time Fourier transform (DTFT) related to differentiation in frequency domain.
Classical windowing scheme such as Hamming window is modified to obtain
derivatives of discrete time Fourier transform coefficients. It has been
mathematically shown that the slope and phase of power spectrum are inherently
incorporated in newly computed cepstrum. Speaker recognition systems based on
our proposed family of window functions are shown to attain substantial and
consistent performance improvement over baseline single tapered Hamming window
as well as recently proposed multitaper windowing technique
Speaker Identification for Swiss German with Spectral and Rhythm Features
We present results of speech rhythm analysis for automatic speaker identification. We expand previous experiments using similar methods for language identification. Features describing the rhythmic properties of salient changes in signal components are extracted and used in an speaker identification task to determine to which extent they are descriptive of speaker variability. We also test the performance of state-of-the-art but simple-to-extract frame-based features. The paper focus is the evaluation on one corpus (swiss german, TEVOID) using support vector machines. Results suggest that the general spectral features can provide very good performance on this dataset, whereas the rhythm features are not as successful in the task, indicating either the lack of suitability for this task or the dataset specificity
Machine Learning Mitigants for Speech Based Cyber Risk
Statistical analysis of speech is an emerging area of machine learning. In this paper, we tackle the biometric challenge of Automatic Speaker Verification (ASV) of differentiating between samples generated by two distinct populations of utterances, those of an authentic human voice and those generated by a synthetic one. Solving such an issue through a statistical perspective foresees the definition of a decision rule function and a learning procedure to identify the optimal classifier. Classical state-of-the-art countermeasures rely on strong assumptions such as stationarity or local-stationarity of speech that may be atypical to encounter in practice. We explore in this regard a robust non-linear and non-stationary signal decomposition method known as the Empirical Mode Decomposition combined with the Mel-Frequency Cepstral Coefficients in a novel fashion with a refined classifier technique known as multi-kernel Support Vector machine. We undertake significant real data case studies covering multiple ASV systems using different datasets, including the ASVSpoof 2019 challenge database. The obtained results overwhelmingly demonstrate the significance of our feature extraction and classifier approach versus existing conventional methods in reducing the threat of cyber-attack perpetrated by synthetic voice replication seeking unauthorised access
Look, Listen and Learn - A Multimodal LSTM for Speaker Identification
Speaker identification refers to the task of localizing the face of a person
who has the same identity as the ongoing voice in a video. This task not only
requires collective perception over both visual and auditory signals, the
robustness to handle severe quality degradations and unconstrained content
variations are also indispensable. In this paper, we describe a novel
multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies
both visual and auditory modalities from the beginning of each sequence input.
The key idea is to extend the conventional LSTM by not only sharing weights
across time steps, but also sharing weights across modalities. We show that
modeling the temporal dependency across face and voice can significantly
improve the robustness to content quality degradations and variations. We also
found that our multimodal LSTM is robustness to distractors, namely the
non-speaking identities. We applied our multimodal LSTM to The Big Bang Theory
dataset and showed that our system outperforms the state-of-the-art systems in
speaker identification with lower false alarm rate and higher recognition
accuracy.Comment: The 30th AAAI Conference on Artificial Intelligence (AAAI-16
Multimodal Subspace Support Vector Data Description
In this paper, we propose a novel method for projecting data from multiple
modalities to a new subspace optimized for one-class classification. The
proposed method iteratively transforms the data from the original feature space
of each modality to a new common feature space along with finding a joint
compact description of data coming from all the modalities. For data in each
modality, we define a separate transformation to map the data from the
corresponding feature space to the new optimized subspace by exploiting the
available information from the class of interest only. We also propose
different regularization strategies for the proposed method and provide both
linear and non-linear formulations. The proposed Multimodal Subspace Support
Vector Data Description outperforms all the competing methods using data from a
single modality or fusing data from all modalities in four out of five
datasets.Comment: 26 pages manuscript (6 tables, 2 figures), 24 pages supplementary
material (27 tables, 10 figures). The manuscript and supplementary material
are combined as a single .pdf (50 pages) fil
- …