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This paper presents an extension of Probabilistic Space Maps (PS-MAPS) to adapt clean acoustic models to a noisy environment. In the presence of noise, the relationship between noisy and clean speech features (MFCC's, HDLA, etc.) is either nonlinear or unknown. Given the relationship between features, traditional methods try to linearize it using approximations. These methods cannot be used for systems...
The model has been successfully applied to bandwidth expansion and speech enhancement. We are in the process of evaluating the performance of the model for automatic speech recognition in noisy environments. This probabilistic model shows great generality and can be applied to other problems such as speech modification and speaker separation.
There has been growing interest in introducing speech as a new modality into the human-computer interface (HCI). Motivated by the multimodal nature of speech, the visual component is considered to yield information that is not always present in the acoustic signal and enables improved system performance over acoustic-only methods, especially in noisy environments. In this paper, we investigate the...
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