Shirin Badiezadegan

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An implementation of a performance monitoring approach to feature channel integration in robust automatic speech recognition is presented. Motivated by psychophysical evidence in human speech perception, the approach combines multiple feature channels using a closed loop criterion relating to the overall performance of the system. The multiple feature(More)
In missing feature based automatic speech recognition (ASR), the role of the spectro-temporal mask in providing an accurate description of the relationship between target speech and environmental noise is critical for minimizing the degradation in ASR word accuracy (WAC) as the signal-to-noise ratio (SNR) decreases. This paper demonstrates the importance of(More)
Data imputation approaches for robust automatic speech recognition reconstruct noise corrupted spectral information by exploiting prior knowledge of the relationship between target speech and background characterized by spectrographic masks. Most of these approaches operate without considering the temporal or spectral trajectories of the spectral(More)
Implementations of two performance monitoring approaches to feature channel integration in robust automatic speech recognition are presented. These approaches combine multiple feature channels, where the first one uses a feed-forward entropy-based criterion and the second one, motivated by psy-chophysical evidence in human speech perception, employs a(More)
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