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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Journal ArticleDOI
Complementing the DTW based speaker verification systems with knowledge of specific regions of interest
Journal ArticleDOI
Unsupervised Learning of Total Variability Embedding for Speaker Verification with Random Digit Strings
Woo Hyun Kang,Nam Soo Kim +1 more
TL;DR: A novel technique for extracting an i-vector-like feature based on the variational autoencoder (VAE), which is trained in an unsupervised manner to obtain a latent variable representing the variability within a Gaussian mixture model (GMM) distribution.
Proceedings ArticleDOI
Person Voice Recognition Methods
Oleksandr Tymchenko,Bohdana Havrysh,Oleksandr O. Tymchenko,Orest Khamula,Bohdan Kovalskyi,Kateryna Havrysh +5 more
TL;DR: This article is devoted to the selection and evaluation of language features used in the tasks of automatic text-independent speaker verification, and the high requirements for the speaker recognition accuracy are required.
Proceedings ArticleDOI
Emotional speaker recognition based on i-vector space model
TL;DR: Results reveal that the i-vector solve the problem of large scale of SVM model and give a promising results for speaker recognition in spontaneous emotional context.
Posted Content
UTD-CRSS Systems for 2016 NIST Speaker Recognition Evaluation
Chunlei Zhang,Fahimeh Bahmaninezhad,Shivesh Ranjan,Chengzhu Yu,Navid Shokouhi,John H. L. Hansen +5 more
TL;DR: The authors used unlabeled in-domain data for centralizing data to alleviate the domain mismatch problem and found the best data set for training LDA/PLDA using newly proposed dimension reduction technique.
References
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Book
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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.
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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
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