Robust automatic speech recognition with missing and unreliable acoustic data
Speech Communication
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
On noise masking for automatic missing data speech recognition: A survey and discussion
Computer Speech and Language
A fused hidden Markov model with application to bimodal speech processing
IEEE Transactions on Signal Processing
A review of speech-based bimodal recognition
IEEE Transactions on Multimedia
Input-output HMMs for sequence processing
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
Automatic speech recognition under adverse noise conditions has been a challenging problem. Under noise conditions when the stationarity assumption is valid, effective techniques have been established to provide excellent recognition accuracies. Under the conditions when this assumption cannot hold, recognition performance de- clines rapidly. Missing data, MD, theory is a promising method for robust automatic speech recognition, ASR, under an y noise condition. Unfortunately, the choice of feature used in the recognizer process is commonly limited to spectral based representations. The combination of recognizers approach to MD ASR allows the use of cepstral based features within the MD framework through a fusion of features mechanism in the pat- tern recognition stage. It was found that under two types of non-stationary noise conditions the combined fused effect, experienced by the fusion process, increased recognition accuracies substantially over traditional MD and cepstral based recognizers.