Practical approaches to speech coding
Practical approaches to speech coding
Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Fundamentals of speech recognition
Fundamentals of speech recognition
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
GTM: the generative topographic mapping
Neural Computation
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Mapping a manifold of perceptual observations
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
Time and frequency filtering of filter-bank energies for robust HMM speech recognition
Speech Communication - Special issue on noise robust ASR
Modern Control Engineering
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Automatic Speech Recognition: The Development of the Sphinx Recognition System
Automatic Speech Recognition: The Development of the Sphinx Recognition System
Linear Prediction of Speech
Non-Linear Dimensionality Reduction
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Classification of coins using an eigenspace approach
Pattern Recognition Letters
Analysis and extension of spectral methods for nonlinear dimensionality reduction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Learning Nonlinear Image Manifolds by Global Alignment of Local Linear Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel PCA for novelty detection
Pattern Recognition
Linear discriminant analysis for improved large vocabulary continuous speech recognition
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Acoustic modeling problem for automatic speech recognition system: conventional methods (Part I)
International Journal of Speech Technology
What if everyone could do it?: a framework for easier spoken dialog system design
Proceedings of the 5th ACM SIGCHI symposium on Engineering interactive computing systems
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Automatic speech recognition (ASR) has made great strides with the development of digital signal processing hardware and software. But despite of all these advances, machines can not match the performance of their human counterparts in terms of accuracy and speed, especially in case of speaker independent speech recognition. So, today significant portion of speech recognition research is focused on speaker independent speech recognition problem. Before recognition, speech processing has to be carried out to get a feature vectors of the signal. So, front end analysis plays a important role. The reasons are its wide range of applications, and limitations of available techniques of speech recognition. So, in this report we briefly discuss the different aspects of front end analysis of speech recognition including sound characteristics, feature extraction techniques, spectral representations of the speech signal etc. We have also discussed the various advantages and disadvantages of each feature extraction technique, along with the suitability of each method to particular application.