| Paper: | SLP-L13.4 | ||
| Session: | Missing Data Methods in Robust Speech Recognition | ||
| Time: | Friday, May 19, 17:30 - 17:50 | ||
| Presentation: | Lecture | ||
| Topic: | Speech and Spoken Language Processing: Feature-based Robust Speech Recognition (e.g., noise, etc) | ||
| Title: | Mask Estimation for Missing Data Recognition using Background Noise Sniffing | ||
| Authors: | Sébastien Demange, Christophe Cerisara, Jean-Paul Haton, LORIA UMR 7503, France | ||
| Abstract: | This paper addresses the problem of spectrographic mask estimation in the context of missing data recognition. At the difference of other denoising methods, missing data recognition does not match the whole spectrum with the acoustic models, but rather considers that some time-frequency pixels are missing, i.e. corrupted by noise. Correctly estimating these masks is very important for missing data recognizers. We propose a new approach that exploits some a priori knowledge about these masks in typical noisy environments to address this difficult challenge. The proposed mask is then obtained by combining these noise dependent masks. The combination is led by an environmental sniffing module that estimates the probability of being in each typical noisy condition. This missing data mask estimation procedure has been integrated in a complete missing data recognizer using bounded marginalization.Our approach is evaluated on the Aurora2 database. | ||
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