A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to algorithms
Ten lectures on wavelets
Multirate systems and filter banks
Multirate systems and filter banks
Trading Accuracy for Simplicity in Decision Trees
Machine Learning
Discrete-time speech signal processing: principles and practice
Discrete-time speech signal processing: principles and practice
Discriminative wavelet packet filter bank selection for pattern recognition
IEEE Transactions on Signal Processing
On signal representations within the Bayes decision framework
Pattern Recognition
Tree pruning with subadditive penalties
IEEE Transactions on Signal Processing
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
TEMPLAR: a wavelet-based framework for pattern learning and analysis
IEEE Transactions on Signal Processing
Minimum probability of error image retrieval
IEEE Transactions on Signal Processing
An Implementation of Rational Wavelets and Filter Design for Phonetic Classification
IEEE Transactions on Audio, Speech, and Language Processing
Optimal pruning with applications to tree-structured source coding and modeling
IEEE Transactions on Information Theory
Entropy-based algorithms for best basis selection
IEEE Transactions on Information Theory - Part 2
A sampling theorem for wavelet subspaces
IEEE Transactions on Information Theory - Part 2
IEEE Transactions on Image Processing
Texture analysis and classification with tree-structured wavelet transform
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
This work proposes using Wavelet-Packet Cepstral coefficients (WPPCs) as an alternative way to do filter-bank energy-based feature extraction (FE) for automatic speech recognition (ASR). The rich coverage of time-frequency properties of Wavelet Packets (WPs) is used to obtain new sets of acoustic features, in which competitive and better performances are obtained with respect to the widely adopted Mel-Frequency Cepstral coefficients (MFCCs) in the TIMIT corpus. In the analysis, concrete filter-bank design considerations are stipulated to obtain most of the phone-discriminating information embedded in the speech signal, where the filter-bank frequency selectivity, and better discrimination in the lower frequency range [200Hz-1kHz] of the acoustic spectrum are important aspects to consider.