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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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Proceedings ArticleDOI
JukeBox: A Multilingual Singer Recognition Dataset.
TL;DR: This work assembles a speaker recognition dataset with multilingual singing voice audio annotated with singer identity, gender, and language labels and uses the current state-of-the-art methods to demonstrate the difficulty of performing speaker recognition on singing voice using models trained on spoken voice alone.
Proceedings ArticleDOI
An Investigation of Speaker Clustering Algorithms in Adverse Acoustic Environments
Meng-Zhen Li,Xiao-Lei Zhang +1 more
TL;DR: Experimental results in various noisy environments show that the MBN based systems perform the best in most cases, while the SC based systems outperform the PCA based systems as well as the original supervector based systems; and AHC is more robust than k-means.
Journal ArticleDOI
Latent discriminative representation learning for speaker recognition
TL;DR: A latent discriminative representation learning method for speaker recognition that outperforms state-of-the-art methods based on the Apollo dataset used in the Fearless Steps Challenge in INTERSPEECH2019 and the TIMIT dataset.
Journal ArticleDOI
Efficient and Privacy-Preserving Speaker Recognition for Cybertwin-Driven 6G
Qi Li,Xiaodong Lin +1 more
TL;DR: An efficient and privacy-preserving speaker recognition scheme for cybertwin-driven 6G, referred to as NEATEN, is proposed, which makes progress on the non-Euclidean distance, such as cosine distance and complicated distance.
Posted Content
Analysis of memory in LSTM-RNNs for source separation.
Jeroen Zegers,Hugo Van hamme +1 more
TL;DR: A memory reset approach is applied to the task of multi-speaker source separation and finds a strong performance effect of short-term linguistic processes and confirms that performance-wise it is sufficient to implement longer memory in deeper layers.
References
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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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