Van Dalen, RC and Gales, MJF (2011) A variational perspective on noise-robust speech recognition. 2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings. pp. 125-130.Full text not available from this repository.
Model compensation methods for noise-robust speech recognition have shown good performance. Predictive linear transformations can approximate these methods to balance computational complexity and compensation accuracy. This paper examines both of these approaches from a variational perspective. Using a matched-pair approximation at the component level yields a number of standard forms of model compensation and predictive linear transformations. However, a tighter bound can be obtained by using variational approximations at the state level. Both model-based and predictive linear transform schemes can be implemented in this framework. Preliminary results show that the tighter bound obtained from the state-level variational approach can yield improved performance over standard schemes. © 2011 IEEE.
|Divisions:||Div F > Machine Intelligence|
|Depositing User:||Cron Job|
|Date Deposited:||02 Sep 2016 17:48|
|Last Modified:||27 Oct 2016 03:26|