Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Speech recognition in noisy environments using first-order vector Taylor series
Speech Communication
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Acoustical and Environmental Robustness in Automatic Speech Recognition
Acoustical and Environmental Robustness in Automatic Speech Recognition
Speech recognition in noisy environments
Speech recognition in noisy environments
Feature Enhancement for Noisy Speech Recognition With a Time-Variant Linear Predictive HMM Structure
IEEE Transactions on Audio, Speech, and Language Processing
A Novel Uncertainty Decoding Rule With Applications to Transmission Error Robust Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
| Hi-index | 0.00 |
In this paper, parameter estimation of a state-space model of noise or noisy speech cepstra is investigated. A blockwise EM algorithm is derived for the estimation of the state and observation noise covariance from noise-only input data. It is supposed to be used during the offline training mode of a speech recognizer. Further a sequential onlineEMalgorithm is developed to adapt the observation noise covariance on noisy speech cepstra at its input. The estimated parameters are then used in model-based speech feature enhancement for noise-robust automatic speech recognition. Experiments on the AURORA4 database lead to improved recognition results with a linear state model compared to the assumption of stationary noise.