This thesis dissertation proposes, as a first step, a detailed introduction to the automatic speech recognition with missing data supported by many bibliographic references. It is shown that the estimation of masks is a crucial step. Indeed, the quality of the estimated masks determines the performance of the recognition system. Improving the reliability of masks is thus an important issue. In a second step, new investigations in the field of Bayesian missing data mask estimation are presented. I propose first new mask models to model dependencies between the masks of different coefficients of a signal. These models are evaluated and compared to a reference model. The results are presented in terms of error of masks, as well as recognition rate. The results show that these dependencies contribute to improving the recognition rate and stress the importance of the temporal context of a mask. Second, I introduce a new missing data mask definition: the masks of contribution. These new masks are evaluated compared to masks commonly used, based on the SNR thresholding. I show how the decoding algorithm can be improved with such a mask definition by refining the likelihood marginalization intervals. The assessment, in the context of data marginalization and in the presence of a stationary noise, shows that the intervals are considerably reduced resulting in a significant improvement of the recognition rate.

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   |wiki=    Wicri/Lorraine
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   |étape=   Merge
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   |texte=   Contributions to automatic speech recognition with missing data
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