Between-Class Covariance Correction For Linear Discriminant Analysis in Language Recognition

@inproceedings{Misra2016BetweenClassCC,
  title={Between-Class Covariance Correction For Linear Discriminant Analysis in Language Recognition},
  author={Abhinav Misra and Q. Zhang and Finnian Kelly and John H. L. Hansen},
  booktitle={Odyssey},
  year={2016}
}
Linear Discriminant Analysis (LDA) is one of the most widely-used channel compensation techniques in current speaker and language recognition systems. In this study, we propose a technique of Between-Class Covariance Correction (BCC) to improve language recognition performance. This approach builds on the idea of WithinClass Covariance Correction (WCC), which was introduced as a means to compensate for mismatch between different development data-sets in speaker recognition. In BCC, we compute… 

Figures and Tables from this paper

Semi-supervised Learning with Generative Adversarial Networks for Arabic Dialect Identification
TLDR
A novel i-vector representation which is based on unsupervised bottleneck features is examined as the feature to identify dialects from Arabic broadcast speech and achieves the state-of-the-art performance in this DID task.

References

SHOWING 1-10 OF 13 REFERENCES
Domain adaptation via within-class covariance correction in I-vector based speaker recognition systems
In this paper we propose a technique of Within-Class Covariance Correction (WCC) for Linear Discriminant Analysis (LDA) in Speaker Recognition to perform an unsupervised adaptation of LDA to an
Source-Normalized LDA for Robust Speaker Recognition Using i-Vectors From Multiple Speech Sources
TLDR
This study provides a thorough analysis of how SN-LDA transforms the i-vector space to reduce source variation and its robustness to varying evaluation and LDA training conditions.
Source-normalised-and-weighted LDA for robust speaker recognition using i-vectors
TLDR
This work proposes a novel source-normalised-and-weighted LDA algorithm developed to improve the robustness of i-vector-based speaker recognition under both mis-matched evaluation conditions and conditions for which insufficient speech resources are available for adequate system development.
Approaches to language identification using Gaussian mixture models and shifted delta cepstral features
TLDR
Two GMM-based approaches to language identification that use shifted delta cepstra (SDC) feature vectors to achieve LID performance comparable to that of the best phone-based systems are described.
Deep Neural Network Approaches to Speaker and Language Recognition
TLDR
This work presents the application of single DNN for both SR and LR using the 2013 Domain Adaptation Challenge speaker recognition (DAC13) and the NIST 2011 language recognition evaluation (LRE11) benchmarks and demonstrates large gains on performance.
Inter dataset variability compensation for speaker recognition
  • Hagai Aronowitz
  • Computer Science
    2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2014
TLDR
This work analyzes the sources of degradation for a particular setup in the context of an i-vector PLDA system and concludes that the main source for degradation is ani-vector dataset shift, which is introduced using the nuisance attribute projection (NAP) method.
The Kaldi Speech Recognition Toolkit
TLDR
The design of Kaldi is described, a free, open-source toolkit for speech recognition research that provides a speech recognition system based on finite-state automata together with detailed documentation and a comprehensive set of scripts for building complete recognition systems.
Introduction to Statistical Pattern Recognition
TLDR
Two approaches to dimensionality reduction, namely feature selection (FS) and feature extraction (FE) are specified, though FS is a special case of FE, they are very different from a practical viewpoint and thus must be considered separately.
LIBSVM: A library for support vector machines
TLDR
Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
Pattern Recognition and Machine Learning (Information Science and Statistics)
Looking for competent reading resources? We have pattern recognition and machine learning information science and statistics to read, not only read, but also download them or even check out online.
...
...