Robust automatic speech recognition with missing and unreliable acoustic data
Speech Communication
Using observation uncertainty for robust speech recognition
Using observation uncertainty for robust speech recognition
Issues with uncertainty decoding for noise robust automatic speech recognition
Speech Communication
A computational auditory scene analysis system for speech segregation and robust speech recognition
Computer Speech and Language
EURASIP Journal on Audio, Speech, and Music Processing
Transforming Binary Uncertainties for Robust Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Exemplar-Based Sparse Representations for Noise Robust Automatic Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
Computer Speech and Language
Uncertainty-based learning of acoustic models from noisy data
Computer Speech and Language
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We present a method of improving automatic speech recognition performance under noisy conditions by using a source separation approach to extract the underlying clean speech signal. The feature enhancement processing is complemented with heuristic estimates of the uncertainty of the source separation, that are used to further assist the recognition. The uncertainty heuristics are converted to estimates of variance for the extracted clean speech using a Gaussian Mixture Model based mapping, and applied in the decoding stage under the observation uncertainty framework. We propose six heuristics, and evaluate them using both artificial and real-world noisy data, and with acoustic models trained on clean speech, a multi-condition noisy data set, and the multi-condition set processed with the source separation front-end. Taking the uncertainty of the enhanced features into account is shown to improve recognition performance when the acoustic models are trained on unenhanced data, while training on enhanced noisy data yields the lowest error rates.