Open Access
MSR Identity Toolbox v1.0: A MATLAB Toolbox for Speaker Recognition Research
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TLDR
The MSR Identity Toolbox is released, which contains a collection of MATLAB tools and routines that can be used for research and development in speaker recognition, and provides many of the functionalities available in other open-source speaker recognition toolkits.Abstract:
We are happy to announce the release of the MSR Identity Toolbox: A MATLAB toolbox for speaker-recognition research. This toolbox contains a collection of MATLAB tools and routines that can be used for research and development in speaker recognition. It provides researchers with a test bed for developing new front-end and back-end techniques, allowing replicable evaluation of new advancements. It will also help newcomers in the field by lowering the "barrier to entry," enabling them to quickly build baseline systems for their experiments. Although the focus of this toolbox is on speaker recognition, it can also be used for other speech related applications such as language, dialect, and accent identification. Additionally, it provides many of the functionalities available in other open-source speaker recognition toolkits (e.g., ALIZEread more
Citations
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Proceedings ArticleDOI
The Relationship Between F0 Synchrony and Speech Convergence in Dyadic Interaction
TL;DR: The results show that high Synchrony of F0 between two speakers leads to greater amount of Convergence, which provides robust support for the idea that SynchronY and Convergence are interrelated processes, particularly in female participants.
DissertationDOI
Articulatory representations to address acoustic variability in speech
TL;DR: A speaker independent acoustic-to-articulatory inversion system that was developed to estimate vocal tract constriction variables (TVs) from speech and the incorporation of articulatory features in state-of-the-art medium vocabulary ASR systems.
Proceedings ArticleDOI
Enhanced forensic speaker verification using multi-run ICA in the presence of environmental noise and reverberation conditions
TL;DR: In this paper, a multi-run independent component analysis (ICA) algorithm is used to enhance the noisy speech signals by choosing the highest signal to interference ratio (SIR) of the mixing matrix from different mixing matrices generated by iterating the fast ICA algorithm for several times.
Posted Content
Sketching for Large-Scale Learning of Mixture Models
TL;DR: In this article, the authors propose a compressive learning framework where they estimate model parameters from a sketch of the training data, which can be computed in a single pass on the training set, and is easily computable on streams or distributed datasets.
Proceedings ArticleDOI
Pronunciation Error Detection for New Language Learners.
TL;DR: An evaluation of pronunciation error detectors on the utterances of second language learners just beginning their studies finds the best error detector achieved detector-annotator agreement of up to κ = .41, near the expected between-annotators agreement.
References
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Book
Introduction to Statistical Pattern Recognition
TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
Journal ArticleDOI
Speaker Verification Using Adapted Gaussian Mixture Models
TL;DR: The major elements of MIT Lincoln Laboratory's Gaussian mixture model (GMM)-based speaker verification system used successfully in several NIST Speaker Recognition Evaluations (SREs) are described.
Journal ArticleDOI
Front-End Factor Analysis for Speaker Verification
TL;DR: An extension of the previous work which proposes a new speaker representation for speaker verification, a new low-dimensional speaker- and channel-dependent space is defined using a simple factor analysis, named the total variability space because it models both speaker and channel variabilities.
Proceedings ArticleDOI
Probabilistic Linear Discriminant Analysis for Inferences About Identity
TL;DR: This paper describes face data as resulting from a generative model which incorporates both within- individual and between-individual variation, and calculates the likelihood that the differences between face images are entirely due to within-individual variability.