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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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Book ChapterDOI
Can Judges Trust the I-Vectors Scores?: A Comparative Study of Voices Comparison in the Forensic Domain
TL;DR: In this paper, the authors adapted an open source platform for Automatic Speaker Recognition (ASR) for use in the forensic domain to estimate and represent the voice as an exhibit.
Proceedings ArticleDOI
Development of Speaker Recognizer Using I-vectors in Two Programming Environments
TL;DR: Comparative evaluation of system performance, in terms of accuracy and computational requirements, for both platforms is presented.
Proceedings ArticleDOI
Improved accent classification combining phonetic vowels with acoustic features
TL;DR: This work combines phonetic knowledge, such as vowels, with enhanced acoustic features to build an improved accent classification system that achieves classification accuracy 54% with input test data as short as 20 seconds, which is competitive to the state of the art in this field.
The Effect of Speech Rate on Automatic Speaker Verification: a Comparative Analysis of GMM-UBM and I-vector Based Methods
TL;DR: The results showed that, despite both methods being significantly affected by mismatch conditions, the performance degradation caused by speech rate variation can be mitigated by the addition of fast speech into the training set.
Book ChapterDOI
Speech Features Evaluation for Small Set Automatic Speaker Verification Using GMM-UBM System
TL;DR: This paper overviews the application sphere of speaker verification systems and illustrates the use of the Gaussian mixture model and the universal background model (GMM-UBM) in an automatic text-independent speaker verification task.
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.